Methods and systems for estimating copy numbers and detecting variants

Abstract

Disclosed herein are systems, devices, and methods for estimating a copy number of the TNXB gene. Also disclosed herein are systems, devices, and methods for variant calling within the TNXB gene, with the variant call being reported in a way that is ambiguous to whether it is placed in TNXB or its paralog, pseudogene TNXA. Also disclosed herein are systems, devices, and methods for estimating a copy number of the C4A gene and / or the C4B gene. Also disclosed herein are systems, devices, and methods for variant calling within the C4A / B genes, with the variant call being reported in a way that is ambiguous to whether it is placed in C4A or C4B.

Description

ILLINC.798WO / IP-2620-PCT PATENT METHODS AND SYSTEMS FOR ESTIMATING COPY NUMBERS AND DETECTING VARIANTS INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 586,647, filed September 29, 2023, which is hereby incorporated by reference in its entirety. BACKGROUND Field

[0002] The disclosed technology relates to the field of nucleic acid sequencing. More particularly, the disclosed technology relates to estimating the number of copies of TNXA / TNXB genes and / or the number of copies of C4A / C4B genes in a genome. The technology also relates to detecting single-nucleotide variants or insertions / deletions in TNXA / TNXB or C4A / C4B genes. Description of the Related Art

[0003] The RCCX genomic region is a segmental duplication, approximately 30,000 nucleotides in length, with at least two or more copies of the repeated segment in most haploid genomes. The RCCX region includes a number of different genes, including C4A, C4B, TNXB, and CYP21A2. Deletions of part or all of RCCX genomic region may contribute to a number of phenotypic impacts, including Systemic Lupus Erythematosus, Ehlers-Danlos Syndrome, and 21-Hydroxylase-Deficient Congenital Adrenal Hyperplasia, through disruption or removal of one or more of the C4A, C4B, TNXB, and CYP21A2 genes. Smaller single- nucleotide variations or insertion / deletion variants of gene regions can also occur in the segment, causing a variety of disease-relevant gene disruptions. The large repeat size and high sequence similarity between copies of the RCCX segment greatly complicates variant identification by standard sequencing-based approaches, as sequenced fragments may be difficult to accurately map to their origin location in the genome due to the large duplicated segmental region.

[0004] Mutations which lead to a TNXB deficiency have been found to be related to Ehlers-Danlos Syndrome (EDS). EDS is a connective tissue disorder that can range from loose joints (hypermobile / classical EDS) to fatal ruptures of the aorta / intestines / uterus (vascular EDS). EDS is usually caused by collagen defects but can also be caused by extracellular matrix defects (as in TNXB-deficient cases). EDS has 13 distinct subtypes, with about 20 implicated genes. Homozygous inactivation of the TNXB genes has been found to cause classical-like EDS. Classical-like EDS mimics classical EDS but with a decreased scarring phenotype and follows autosomal recessive inheritance. Complete TNXB deficiency is rare and survivable, with 30 cases identified in the literature as of 2019. Heterozygous inactivation of TNXB causes hypermobile EDS, which is the least severe form of the disease and the most common. Hypermobile EDS follows autosomal dominant inheritance.

[0005] Defects in the C4A / C4B (collectively, C4) genes lead to a C4 deficiency which has been correlated with increased risk of insulin-dependent diabetes, systematic lupus erythematosus (SLE), and various autoimmune disorders. Mutations in C4 genes which results in overexpression “high C4”, such as due to duplications of the C4A and / or C4B genes, has been found to be correlated with increased risk of schizophrenia and bipolar disorder. C4A binds preferentially to immune complexes, while C4B binds preferentially to hydroxyl groups. SUMMARY

[0006] In one aspect, disclosed herein are computer-implemented methods of estimating a copy number of a TNXB gene located within a RCCX region of a human genome. In some embodiments, the method comprises estimating a copy number of RCCX modules in a nucleic acid sample from a human genome; counting sequence reads which include a TNXA- specific base at one or more pre-determined differentiating sites of a TNXB gene and a TNXA pseudogene within the RCCX region to obtain a TNXA sequence read count; counting sequence reads which include a TNXB-specific base at the one or more pre-determined differentiating sites to obtain a TNXB sequence read count; and estimating the copy number of the TNXB gene based on the estimated copy number of RCCX modules, the TNXB sequence read count, and the TNXA sequence read count.

[0007] In some embodiments, estimating the copy number of the TNXB gene comprises calculating a probability of a TNXB copy number of 0 up to a number correspondingto the estimated copy number of RCCX modules, based on the TNXB sequence read count and the TNXA sequence read count.

[0008] In some embodiments, estimating the copy number of the TNXB gene comprises calculating a probability of a TNXB copy number based on a prior probability of a TNXB copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules.

[0009] In some embodiments, estimating the copy number of a TNXB gene comprises calculating a Poisson posterior probability.

[0010] In some embodiments, estimating a copy number of RCCX modules comprises counting sequence reads which align to an RCCX region of the human genome. In some embodiments, estimating a copy number of RCCX modules comprises counting sequence reads which align to a C4A gene, a CYP21A1P gene, a TNXA pseudogene, a C4B gene, a CYP21A2 gene, or a TNXB gene in the human genome. In some embodiments, estimating a copy number of RCCX modules comprises counting sequence reads which align to a region corresponding to positions chr6: 32024461-chr6:32043719 of reference genome hg38, chr6: 31991723-chr6: 32010985 of reference genome hg38, chr6:31992238-chr6: 32011496 of reference genome hg19, or chr6:31959500-chr6:31978762 of reference genome hg19.

[0011] In some embodiments, estimating the copy number of RCCX modules comprises counting sequence reads which align to diploid regions in the human genome within the nucleic acid sample. In some embodiments, estimating the copy number of RCCX modules comprises normalizing the count of the sequence reads which align to a RCCX region of the human genome by the sequence reads which align to a plurality of diploid regions of the human genome. In some embodiments, said normalizing comprises GC correction. In some embodiments, estimating a copy number of RCCX modules comprises binning the normalized count of the sequence reads which align to a RCCX region of the human genome using a Gaussian mixture model.

[0012] In some embodiments, the method further comprises creating a digital file including the estimated copy number of the TNXB gene.

[0013] In some embodiments, the one or more of pre-determined differentiating sites comprise a site corresponding to chr6:32046056 of the TNXB gene or corresponding position chr6:32013202 in the TNXA pseudogene, in reference genome hg38.

[0014] In another aspect, disclosed herein are computer-implemented methods of detecting one or more single-nucleotide variants or indels in a TNXB gene or a TNXA pseudogene in a nucleic acid sample. In some embodiments, the method includes determining sequence reads from the nucleic acid sample; obtaining sequence reads which align to a site of a single-nucleotide variant or indel in a TNXB gene in a human genome of the nucleic acid sample, obtaining sequence reads which align to a corresponding site of the single-nucleotide variant or indel in a TNXA pseudogene in human genome of the nucleic acid sample, counting sequence reads which contain a base corresponding to an alternative allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the TNXB gene and sequence reads which align to the TNXA pseudogene; counting sequence reads which contain a base corresponding to a reference allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the TNXB gene and sequence reads which align to the TNXA pseudogene; and based on the count of the sequence reads which contain a base corresponding to an alternative allele and the count of sequence reads which contain a base corresponding to a reference allele, detecting the single-nucleotide variant or indel, wherein the detection of the single-nucleotide variant or indel is not specific to the TNXB gene or the TNXA pseudogene.

[0015] In some embodiments, the sequence reads are less than 400 bp in length. In some embodiments, the one or more single-nucleotide variants or indels comprise TNXB_c.12220C>T or TNXB_c.10623_10624del. In some embodiments, the method further comprises creating a digital file including a variant call corresponding to the single-nucleotide variant or indel.

[0016] In a further aspect, disclosed herein are computer-implemented methods of estimating a copy number of a C4A gene or a C4B gene located within a RCCX region of a human genome. In some embodiments, the method includes: estimating a copy number of RCCX modules in a nucleic acid sample from a human genome; counting sequence reads which include a C4A-specific base at one or more pre-determined differentiating sites of a C4Agene and a C4B gene within the RCCX region to obtain a C4A sequence read count; counting sequence reads which include a C4B-specific base at the one or more pre-determined differentiating sites to obtain a C4B sequence read count, and estimating the copy number of the C4A gene or the copy number of the C4B gene based on the estimated copy number of RCCX modules, the C4A sequence read count, and the C4B sequence read count.

[0017] In some embodiments, estimating the copy number of the C4A gene or the copy number of the C4B gene comprises calculating a probability of a C4A copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules, based on the C4A sequence read count and the C4B sequence read count, or calculating a probability of a C4B copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules, based on the C4A sequence read count and the C4B sequence read count.

[0018] In some embodiments, estimating the copy number of the C4A gene or the copy number of the C4B gene comprises calculating a probability of a C4A copy number or a C4B copy number based on a prior probability of a C4A copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules, or a prior probability of a C4B copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules.

[0019] In some embodiments, estimating the copy number of the C4A gene or the copy number of the C4B gene comprises a calculating Poisson posterior probability.

[0020] In some embodiments, estimating a copy number of RCCX modules comprises counting sequence reads which align to the RCCX region of the human genome. In some embodiments, estimating a copy number of RCCX modules comprises counting sequence reads which align to a C4A gene, a CYP21A1P gene, a TNXA pseudogene, a C4B gene, a CYP21A2 gene, or a TNXB gene in the human genome. In some embodiments, estimating a copy number of RCCX modules comprises counting sequence reads which align to a region corresponding to positions chr6: 32024461-chr6:32043719 of reference genome hg38, chr6: 31991723-chr6: 32010985 of reference genome hg38, chr6:31992238-chr6: 32011496 of reference genome hg19, or chr6:31959500-chr6:31978762 of reference genome hg19.

[0021] In some embodiments, estimating the copy number of the C4A gene or the copy number of the C4B gene further comprises counting sequence reads which align to diploid regions in the human genome within the nucleic acid sample.

[0022] In some embodiments, estimating a copy number of RCCX modules comprises normalizing the count of the sequence reads which align to a RCCX region of the human genome by the count of sequence reads which align to a plurality of diploid regions of the human genome. In some embodiments, said wherein said normalizing comprises GC correction. In some embodiments, estimating a copy number of RCCX modules comprises binning the normalized count of the sequence reads which align to a RCCX region of the human genome using a Gaussian mixture model.

[0023] In some embodiments, the method further comprises creating a digital file including the estimated copy number of the C4A gene or the estimated copy number of the C4B gene.

[0024] In some embodiments, the one or more pre-determined differentiating sites comprise a site corresponding to chr6:32028821, chr6:32028824, chr6:32028832, chr6:32028835, or chr6:32028837 of the C4B gene or a corresponding position in the C4A gene, in reference genome hg38.

[0025] In a further aspect, disclosed herein are computer-implemented methods of detecting one or more single-nucleotide variants or indels in a C4A gene or a C4B gene in a nucleic acid sample. In some embodiments, the method includes determining sequence reads from the nucleic acid sample; obtaining sequence reads which align to a site of a single- nucleotide variant or indel in a C4A gene in a human genome of the nucleic acid sample, obtaining sequence reads which align to a corresponding site of the single-nucleotide variant or indel in a C4B gene in human genome of the nucleic acid sample, counting sequence reads which contain a base corresponding to an alternative allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the C4A gene and sequence reads which align to the C4B gene; counting sequence reads which contain a base corresponding to a reference allele at the site of the single- nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the C4A gene and sequence reads which align to the C4B gene; and based on the count of the sequence reads which contain a base corresponding to an alternative alleleand the count of sequence reads which contain a base corresponding to a reference allele, detecting the single-nucleotide variant or indel, wherein the detection of the single-nucleotide variant or indel is not specific to the C4A gene or the C4B gene.

[0026] In some embodiments, the sequence reads are less than 400 bp in length. In some embodiments, the one or more single-nucleotide variants or indels comprise C4A_insCT or C4B_3676G>A. In some embodiments, the method further comprises creating a digital file including a variant call corresponding to the single-nucleotide variant or indel.

[0027] In a further aspect, disclosed herein are electronic systems for estimating a copy number of a TNXB gene located within a RCCX region of a human genome. In some embodiments, the system comprises a processor configured to perform a method comprising: estimating a copy number of RCCX modules in a nucleic acid sample from a human genome; counting sequence reads which include a TNXA-specific base at one or more pre-determined differentiating sites of a TNXB gene and a TNXA pseudogene within the RCCX region to obtain a TNXA sequence read count; counting sequence reads which include a TNXB-specific base at the one or more pre-determined differentiating sites to obtain a TNXB sequence read count; and estimating the copy number of the TNXB gene based on the estimated copy number of RCCX modules, the TNXB sequence read count, and the TNXA sequence read count.

[0028] In a further aspect, disclosed herein are electronic systems for detecting one or more single-nucleotide variants or indels in a TNXB gene or a TNXA pseudogene in a nucleic acid sample. In some embodiments, the system comprises a processor configured to perform a method comprising: determining sequence reads from the nucleic acid sample; obtaining sequence reads which align to a site of a single-nucleotide variant or indel in a TNXB gene in a human genome of the nucleic acid sample, obtaining sequence reads which align to a corresponding site of the single-nucleotide variant or indel in a TNXA pseudogene in human genome of the nucleic acid sample, counting sequence reads which contain a base corresponding to an alternative allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the TNXB gene and sequence reads which align to the TNXA pseudogene; counting sequence reads which contain a base corresponding to a reference allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the TNXB gene and sequence reads which align to the TNXA pseudogene; and based on the countof the sequence reads which contain a base corresponding to an alternative allele and the count of sequence reads which contain a base corresponding to a reference allele, detecting the single-nucleotide variant or indel, wherein the detection of the single-nucleotide variant or indel is not specific to the TNXB gene or the TNXA pseudogene.

[0029] In a further aspect, disclosed herein are electronic systems for estimating a copy number of a C4A gene or a C4B gene located within a RCCX region of a human genome. In some embodiments, the system comprises a processor configured to perform a method comprising: estimating a copy number of RCCX modules in a nucleic acid sample from a human genome; counting sequence reads which include a C4A-specific base at one or more pre-determined differentiating sites of a C4A gene and a C4B gene within the RCCX region to obtain a C4A sequence read count; counting sequence reads which include a C4B-specific base at the one or more pre-determined differentiating sites to obtain a C4B sequence read count, and estimating the copy number of the C4A gene or the copy number of the C4B gene based on the estimated copy number of RCCX modules, the C4A sequence read count, and the C4B sequence read count.

[0030] In a further aspect, disclosed herein are electronic systems for detecting one or more single-nucleotide variants or indels in a C4A gene or a C4B gene in a nucleic acid sample. In some embodiments, the system comprises a processor configured to perform a method comprising: determining sequence reads from the nucleic acid sample; obtaining sequence reads which align to a site of a single-nucleotide variant or indel in a C4A gene in a human genome of the nucleic acid sample, obtaining sequence reads which align to a corresponding site of the single-nucleotide variant or indel in a C4B gene in human genome of the nucleic acid sample, counting sequence reads which contain a base corresponding to an alternative allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the C4A gene and sequence reads which align to the C4B gene; counting sequence reads which contain a base corresponding to a reference allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the C4A gene and sequence reads which align to the C4B gene; and based on the count of the sequence reads which contain a base corresponding to an alternative allele and the count of sequence reads which contain a base corresponding to a reference allele, detecting the single-nucleotide variant or indel,wherein the detection of the single-nucleotide variant or indel is not specific to the C4A gene or the C4B gene. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Features of examples of the present disclosure will become apparent by reference to the following detailed description and drawings, in which like reference numerals correspond to similar, though perhaps not identical, components. For the sake of brevity, reference numerals or features having a previously described function may or may not be described in connection with other drawings in which they appear.

[0032] FIG. 1 schematically illustrates the RCCX region and RCCX modules of a human genome.

[0033] FIG. 2A is a flow diagram that schematically illustrates methods of estimating a copy number of a TNXB gene located within the RCCX region of a human genome.

[0034] FIG.2B is a flow diagram that further schematically illustrates a process of estimating a copy number of RCCX modules.

[0035] FIG. 2C is a flow diagram that schematically illustrates methods of estimating a copy number of a C4A gene or a C4B gene located within the RCCX region of a human genome.

[0036] FIG.3A is a block diagram of an exemplary sequencing system that may be used to perform the disclosed methods.

[0037] FIG.3B is a block diagram of an exemplary computing device that may be used in connection with the exemplary sequencing system of FIG.3A.

[0038] FIG. 4 is a line graph illustrating a comparison of RCCX module copy number estimation with copy number calls from optical mapping from BIONANO GENOMICS, INC. (San Diego, CA) optical mapping technology. DETAILED DESCRIPTION

[0039] All patents, patent applications, and other publications, including all sequences disclosed within these references, referred to herein are expressly incorporated herein by reference, to the same extent as if each individual publication, patent or patentapplication was specifically and individually indicated to be incorporated by reference. All documents cited are, in relevant part, incorporated herein by reference in their entireties for the purposes indicated by the context of their citation herein. However, the citation of any document is not to be construed as an admission that it is prior art with respect to the present disclosure. Overview

[0040] The TNXB gene, and its paralog the TNXA pseudogene, and the C4A gene and its paralog the C4B gene, lie within the RCCX region, which is schematically illustrated in FIG. 1, and which includes two RCCX modules. As illustrated in FIG. 1, the first RCCX module 101 includes part of the STK19 gene, the C4A gene, the CYP21A1P pseudogene, and the TNXA pseudogene, and the second RCCX module 102 includes the STK19P pseudogene, the C4B gene, the CYP21A2 gene, and part of the TNXB gene.

[0041] Recombination events occur at a relatively high rate within the RCCX region due to high sequence homology between genes and their respective paralogs. The number of copies of each gene within the RCCX region (including its duplications) is difficult to estimate due to the high sequence homology between the genes and their respective pseudogenes or paralogs. For example, when obtaining DNA sequencing reads from relatively short segments of the RCCX region, the sequence reads may be mapped to the wrong gene / paralog due to the high homology between each of these repeated genes, pseudogenes and paralogs.

[0042] For example, the C4A gene and the C4B gene are almost identical, only being different in five nucleotide positions. These are the only fixed differentiating sites between the C4A and C4B genes, and these sites occur in a 16-bp region. Thus, sequence reads derived from the C4A gene can be difficult to distinguish from sequence reads derived from the C4B gene, and accordingly, estimating the copy numbers of the C4A gene and the C4B gene in a genomic sample may be difficult.

[0043] Determining the copy number of a gene in the RCCX region may be important in order to determine the disease state of a subject, for example since there are phenotypic effects of different copy numbers of the TNXB gene, the C4A gene, and / or the C4B gene.

[0044] Other small variants within the TNXB gene, the C4A gene, and / or the C4B gene (single-nucleotide and relatively short insertion / deletion events) can also lead to phenotypic effects, such as decreased activity of these genes. These variants may occur in regions of the TNXB gene, the C4A gene, or the C4B gene where the nucleotide sequence is identical or nearly identical to its respective paralog. This may make variant detection challenging as reads sequenced from either the gene or its paralog may lack identifying markers, meaning that during an assembly process following sequencing they may be randomly assigned to the wrong gene. This can result in weak and ambiguous evidence for a variant in either location, which can mean missed or low-confidence variant calls.

[0045] Described herein are methods and systems for estimating a copy number of a TNXB gene located within a RCCX region of a human genome, in a nucleic acid sample taken from one or more subjects. Also disclosed herein are methods and systems for detecting one or more single-nucleotide variants or indels in a TNXB gene or a TNXA pseudogene in a nucleic acid sample. In some embodiments, the disclosed systems and methods receive sequence reads which align to a RCCX region found in a biological sample taken from a subject. Once the sequence reads are received, a copy number of the RCCX modules of the RCCX region can be estimated. Estimating the RCCX copy number may include counting the sequence reads that align to the RCCX region of a reference genome.

[0046] In some embodiments, the disclosed systems and methods count sequence reads which include a TNXA-specific base at one or more pre-determined differentiating sites. A “TNXA-specific base” is a nucleotide which is unique to the TNXA pseudogene and is not found in a similar position in the TNXB gene. In some embodiments, pre-determined differentiating sites may include positions in the nucleic acid sequence of the TNXB gene and the TNXA pseudogene where the nucleotides differ by at least one base between the TNXB gene and the TNXA pseudogene, and which difference is pre-determined to be fixed in a population. Thus, these pre-determined differentiating sites may be used to determine whether a particular sequence read corresponds to the TNXB gene or the TNXA pseudogene. In some embodiments, sequence reads which include a TNXA-specific base at one or more pre-determined differentiating sites are counted to obtain a TNXA sequence read count.

[0047] In some embodiments, the disclosed systems and methods count sequence reads which include a TNXB-specific base at the one or more pre-determined differentiating sites to obtain a TNXB sequence read count.

[0048] In some embodiments, the disclosed systems and methods estimate the copy number of the TNXB genes in a nucleic acid sample based on the estimated copy number of RCCX modules, the TNXB sequence read count, and the TNXA sequence read count. For example, the disclosed systems and methods may calculate a probability of a TNXB copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules, based on the TNXB sequence read count and the TNXA sequence read count, and in some embodiments, based on a prior probability of a TNXB copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules.

[0049] In some embodiments, the disclosed systems and methods detect one or more single-nucleotide variants or insertions / deletions (indels) in a TNXB gene or a TNXA pseudogene in a nucleic acid sample. In some embodiments, the disclosed systems and methods counting sequence reads which contain an alternative allele base, and sequence reads which contain reference allele base, at the site of the single-nucleotide variant or indel. In some embodiments, the disclosed systems and methods count both sequence reads which align to the TNXB gene over the site and sequence reads which align to a corresponding position in the TNXA pseudogene, to allow for variant calling that is not specific to (i.e., is ambiguous to) the TNXB gene or the TNXA pseudogene.

[0050] Also disclosed herein are methods and systems for estimating the number of copies of a C4A gene or a C4B gene located within the RCCX region of a human genome. The analysis can be taken from a nucleic acid sample from one or more subjects and can include methods and systems for detecting one or more single-nucleotide variants or indels in a C4A gene or a C4B gene in the nucleic acid sample. The disclosed methods and systems for copy number estimation and small variant calling in the C4A gene and / or the C4B gene may be performed using steps similar to those described above with reference to the TNXA pseudogene and / or the TNXB gene. For example, the systems and methods may count sequence reads which include a C4A gene and / or the C4B gene-specific base at one or more pre- determined differentiating sites within the C4A or C4B gene regions. For example, the disclosed systems and methods may calculate a probability of a C4A copy number of 0 up to anumber corresponding to the estimated copy number of RCCX modules, based on the C4B sequence read count and the C4A sequence read count, and in some embodiments, based on a prior probability of a C4A copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules. For example, the disclosed systems and methods may calculate a probability of a C4B copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules, based on the C4B sequence read count and the C4A sequence read count, and in some embodiments, based on a prior probability of a C4B copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules. Definitions

[0051] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs. See, for example, Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, NY 1994); Sambrook et al., Molecular Cloning, A Laboratory Manual, Cold Spring Harbor Press (Cold Spring Harbor, NY 1989). For purposes of the present disclosure, the following terms are defined below.

[0052] As used herein, a “nucleotide” includes a nitrogen containing heterocyclic base, a sugar, and one or more phosphate groups. Nucleotides are monomeric units of a nucleic acid sequence. Examples of nucleotides include, for example, ribonucleotides or deoxyribonucleotides. In ribonucleotides (RNA), the sugar is a ribose, and in deoxyribonucleotides (DNA), the sugar is a deoxyribose, i.e., a sugar lacking a hydroxyl group that is present at the 2' position in ribose. The nitrogen containing heterocyclic base can be a purine base or a pyrimidine base. Purine bases include adenine (A) and guanine (G), and modified derivatives or analogs thereof. Pyrimidine bases include cytosine (C), thymine (T), and uracil (U), and modified derivatives or analogs thereof. The C-1 atom of deoxyribose is bonded to N-1 of a pyrimidine or N-9 of a purine. The phosphate groups may be in the mono- , di-, or tri-phosphate form. These nucleotides may be natural nucleotides, but it is to be further understood that non-natural nucleotides, modified nucleotides or analogs of the aforementioned nucleotides can also be used.

[0053] As used herein, “base” or “nucleobase” is a heterocyclic base such as adenine, guanine, cytosine, thymine, uracil, inosine, xanthine, hypoxanthine, or a heterocyclicderivative, analog, or tautomer thereof. A nucleobase can be naturally occurring or synthetic. Non-limiting examples of nucleobases are adenine, guanine, thymine, cytosine, uracil, xanthine, hypoxanthine, 8-azapurine, purines substituted at the 8 position with methyl or bromine, 9-oxo-N6-methyladenine, 2-aminoadenine, 7-deazaxanthine, 7-deazaguanine, 7- deaza-adenine, N4-ethanocytosine, 2,6- diaminopurine, N6-ethano-2,6-diaminopurine, 5- methylcytosine, 5-(C3-C6)- alkynylcytosine, 5-fluorouracil, 5-bromouracil, thiouracil, pseudoisocytosine, 2-hydroxy-5-methyl-4-triazolopyridine, isocytosine, isoguanine, inosine, 7,8-dimethylalloxazine, 6-dihydrothymine, 5,6-dihydrouracil, 4-methyl-indole, ethenoadenine and the non-naturally occurring nucleobases described in U.S. Pat. Nos. 5,432,272 and 6,150,510 and PCT applications WO 92 / 002258, WO 93 / 10820, WO 94 / 22892, and WO 94 / 24144, and Fasman (“Practical Handbook of Biochemistry and Molecular Biology”, pp. 485-494, 1989, CRC Press, Boca Raton, LO), all herein incorporated by reference in their entireties.

[0054] The term “nucleic acid” or “polynucleotide” refers to a deoxyribonucleotide or ribonucleotide polymer in either single- or double-stranded form, and unless otherwise limited, encompasses known analogs of natural nucleotides that hybridize to nucleic acids in manner similar to naturally occurring nucleotides, such as peptide nucleic acids (PNAs) and phosphorothioate DNA. Unless otherwise indicated, a particular nucleic acid sequence includes the complementary sequence thereof. Nucleotides include, but are not limited to, ATP, dATP, CTP, dCTP, GTP, dGTP, UTP, TTP, dUTP, 5-methyl-CTP, 5-methyl-dCTP, ITP, dITP, 2-amino-adenosine-TP, 2-amino-deoxyadenosine-TP, 2-thiothymidine triphosphate, pyrrolo-pyrimidine triphosphate, and 2-thiocytidine, as well as the alphathiotriphosphates for all of the above, and 2ƍ-O-methyl-ribonucleotide triphosphates for all the above bases. Modified bases include, but are not limited to, 5-Br-UTP, 5-Br-dUTP, 5-F-UTP, 5-F-dUTP, 5-propynyl dCTP, and 5-propynyl-dUTP.

[0055] As used herein the term “chromosome” refers to the heredity-bearing gene carrier of a living cell, which is derived from chromatin strands comprising DNA and protein components (especially histones). The conventional internationally recognized individual human genome chromosome numbering system is employed herein.

[0056] A “genome” refers to the complete genetic information of an organism or virus, expressed in nucleic acid sequences.

[0057] As used herein, the term “reference genome” or “reference sequence” refers to any particular known genome sequence, whether partial or complete, of any organism or virus which may be used to reference identified sequences from a subject. For example, a reference genome used for human subjects as well as many other organisms is found at the National Center for Biotechnology Information at ncbi.nlm.nih.gov. In various embodiments, the reference sequence is significantly larger than the reads that are aligned to it. For example, it may be at least about 100 times larger, or at least about 1000 times larger, or at least about 10,000 times larger, or at least about 105 times larger, or at least about 106 times larger, or at least about 107 times larger. In one example, the reference sequence is that of a full-length genome. Such sequences may be referred to as genomic reference sequences. For example, the reference sequence can be a reference human genome sequence, such as hg19 (for example, available at GenBank assembly accession GCA_000001405.1) or hg38 (for example, available at GenBank assembly accession GCA_000001405.15). In another example, the reference sequence is limited to a specific human chromosome such as chromosome 13. In some embodiments, a reference Y chromosome is the Y chromosome sequence from human genome version hg19. Such sequences may be referred to as chromosome reference sequences. Other examples of reference sequences include genomes of other species, as well as chromosomes, sub-chromosomal regions (such as strands), etc., of any species. In various embodiments, the reference sequence is a consensus sequence or other combination derived from multiple individuals. However, in certain applications, the reference sequence may be taken from a particular individual.

[0058] The term “nucleic acid sample” herein may refer to a sample, typically derived from one or more biological fluids, cells, tissues, organs, or organisms, comprising a nucleic acid or a mixture of nucleic acids comprising at least one nucleic acid sequence that is to be screened for copy number variation. In certain embodiments the nucleic acid sample comprises at least one nucleic acid sequence whose copy number is suspected of having undergone variation. Such samples may include, but are not limited to sputum / oral fluid, amniotic fluid, blood, a blood fraction, or fine needle biopsy samples (such as surgical biopsy, fine needle biopsy, etc.), urine, peritoneal fluid, pleural fluid, and the like. Although the sample is often taken from a human subject (such as a patient), the sample may be from any mammal, including, but not limited to dogs, cats, horses, goats, sheep, cattle, pigs, etc. The sample maybe used directly as obtained from the biological source or following a pretreatment to modify the character of the sample. For example, such pretreatment may include preparing plasma from blood, diluting viscous fluids and so forth. Methods of pretreatment may also involve, but are not limited to, filtration, precipitation, dilution, distillation, mixing, centrifugation, freezing, lyophilization, concentration, amplification, nucleic acid fragmentation, inactivation of interfering components, the addition of reagents, lysing, etc. If such methods of pretreatment are employed with respect to the sample, such pretreatment methods are typically such that the nucleic acid(s) of interest remain in the test sample, sometimes at a concentration proportional to that in an untreated test sample (such as namely, a sample that is not subjected to any such pretreatment method(s)). Such “treated” or “processed” samples are still considered to be biological “test” samples with respect to the methods described herein. A “nucleic acid sample” may also include nucleic acid sequence information stored in a memory, and which was originally obtained from a source such as one or more biological fluids, cells, tissues, organs, or organisms.

[0059] The term “read” or “sequence read” (or sequencing reads) refer to a sequence obtained from a portion of a nucleic acid sample. A read may be represented by a string of nucleotides sequenced from any part or all of a nucleic acid molecule. Typically, though not necessarily, a read represents a short sequence of contiguous base pairs in the sample. The read may be represented symbolically by the base pair sequence (in A, T, C, or G) of the sample portion. It may be stored in a memory device and processed as appropriate to determine whether it matches a reference sequence or meets other criteria. A read may be obtained directly from a sequencing apparatus or indirectly from stored sequence information concerning the sample. In some cases, a read is a DNA sequence of sufficient length (such as at least about 25 bp) that can be used to identify a larger sequence or region, for example, which can be aligned and specifically assigned to a chromosome or genomic region or gene. For example, a sequence read may be a short string of nucleotides (such as 20-150 bases) sequenced from a nucleic acid fragment, a short string of nucleotides at one or both ends of a nucleic acid fragment, or the sequencing of the entire nucleic acid fragment that exists in the biological sample. Sequence reads may be obtained by any method known in the art. For example, a sequence read may be obtained in a variety of ways, such as using sequencing techniques or using probes, such as in hybridization arrays or capture probes, or amplificationtechniques, such as the polymerase chain reaction (PCR) or linear amplification using a single primer or isothermal amplification. Sequence reads can be generated by techniques such as sequencing by synthesis, sequencing by binding, or sequencing by ligation. Sequence reads can be generated using instruments such as MINISEQ, MISEQ, NEXTSEQ, HISEQ, and NOVASEQ sequencing instruments from Illumina, Inc. (San Diego, CA).

[0060] The term “sequencing depth,” as used herein, generally refers to the number of times a locus is covered by a sequence read aligned to the locus. The locus may be as small as a nucleotide, or as large as a chromosome arm, or as large as the entire genome. Sequencing depth can be expressed as 50×, 100×, etc., where “×” refers to the number of times a locus is covered with a sequence read. Sequencing depth can also be applied to multiple loci, or the whole genome, in which case x can refer to the mean number of times the loci or the haploid genome, or the whole genome, respectively, is sequenced. When a mean depth is quoted, the actual depth for different loci included in the dataset spans over a range of values. Ultra-deep sequencing can refer to at least 100× in sequencing depth.

[0061] As used herein, the terms “aligned,” “alignment,” or “aligning” refer to the process of comparing a read or tag to a reference sequence and thereby determining the likelihood of the reference sequence contains the read sequence. If the reference sequence contains the read, the read may be mapped to the reference sequence or, in certain embodiments, to a particular location in the reference sequence. For example, the alignment of a read to the reference sequence for human chromosome 13 will tell the likelihood of the read is present in the reference sequence for chromosome 13. In some cases, an alignment additionally indicates a location where the read or tag maps to in the reference sequence. For example, if the reference sequence is the whole human genome sequence, an alignment may indicate that a read is present on chromosome 13, and may further indicate that the read is on a particular strand and / or site of chromosome 13. A “site” may be a unique position on a polynucleotide sequence or a reference genome (i.e., chromosome ID, chromosome position and orientation). In some embodiments, a site may provide a position for a residue, a sequence tag, or a segment on a sequence.

[0062] Aligned reads or tags are one or more sequences that are identified as a match in terms of the order of their nucleic acid molecules to a known sequence from a reference genome. Alignment can be done manually, although it is typically implemented bya computer algorithm, as it would be impossible to align reads in a reasonable time period for implementing the methods disclosed herein. The matching of a sequence read in aligning can be a 100% sequence match or less than 100% (non-perfect match).

[0063] Alignment may be performed by modifications and / or combinations of methods such as Burrows-Wheeler Aligner (BWA), iSAAC, BarraCUDA, BFAST, BLASTN, BLAT, Bowtie, CASHX, Cloudburst, CUDA-EC, CUSHAW, CUSHAW2, CUSHAW2-GPU, drFAST, ELAND, ERNE, GNUMAP, GEM, GensearchNGS, GMAP and GSNAP, Geneious Assembler, LAST, MAQ, mrFAST and mrsFAST, MOM, MOSAIK, MPscan, Novoaligh & NovoalignCS, NextGENe, Omixon, PALMapper, Partek, PASS, PerM, PRIMEX, QPalma, RazerS, REAL, cREAL, RMAP, rNA, RT Investigator, Segemehl, SeqMap, Shrec, SHRiMP, SLIDER, SOAP, SOAP2, SOAP3 and SOAP3-dp, SOCS, SSAHA and SSAHA2, Stampy, SToRM, Subread and Subjunc, Taipan, UGENE, VelociMapper, XpressAlign, and ZOOM.

[0064] The term “mapping” used herein refers to specifically assigning a sequence read to a larger sequence, such as a reference genome, by alignment.

[0065] A “genetic variation” or “genetic alteration” refers to a particular genotype present in certain individuals, and often a genetic variation is present in a statistically significant sub-population of individuals. The presence or absence of a genetic variance can be determined using a method or apparatus described herein. In certain embodiments, the presence or absence of one or more genetic variations is determined according to an outcome provided by methods and apparatuses described herein. In some embodiments, a genetic variation is a chromosome abnormality (such as aneuploidy), partial chromosome abnormality or mosaicism, each of which is described in greater detail herein. Non-limiting examples of genetic variations include one or more deletions (such as micro-deletions), duplications (such as micro-duplications), insertions, mutations, polymorphisms (such as single-nucleotide polymorphisms), fusions, repeats (such as short tandem repeats), distinct methylation sites, distinct methylation patterns, the like and combinations thereof. An insertion, repeat, deletion, duplication, mutation or polymorphism can be of any length, and in some embodiments, is about 1 base or base pair (bp) to about 250 megabases (Mb) in length. In some embodiments, an insertion, repeat, deletion, duplication, mutation or polymorphism is about 1 base or base pair (bp) to about 1,000 kilobases (kb) in length (for example about 10 bp, 50 bp, 100 bp, 500 bp, 1 kb, 5 kb, 10 kb, 50 kb, 100 kb, 500 kb, or 1000 kb in length).

[0066] A genetic variation is sometimes a deletion. In certain embodiments a deletion is a mutation (such as a genetic aberration) in which a part of a chromosome or a sequence of DNA is missing. A deletion is often the loss of genetic material. Any number of nucleotides can be deleted. A deletion can comprise the deletion of one or more entire chromosomes, a segment of a chromosome, an allele, a gene, an intron, an exon, any non- coding region, any coding region, a segment thereof or combination thereof. A deletion can comprise a microdeletion. A deletion can comprise the deletion of a single base.

[0067] A genetic variation is sometimes a genetic duplication. In certain embodiments a duplication is a mutation (such as a genetic aberration) in which a part of a chromosome or a sequence of DNA is copied and inserted back into the genome. In certain embodiments a genetic duplication (i.e., duplication) is any duplication of a region of DNA. In some embodiments a duplication is a nucleic acid sequence that is repeated, often in tandem, within a genome or chromosome. In some embodiments a duplication can comprise a copy of one or more entire chromosomes, a segment of a chromosome, an allele, a gene, an intron, an exon, any non-coding region, any coding region, segment thereof or combination thereof. A duplication can comprise a microduplication. A duplication sometimes comprises one or more copies of a duplicated nucleic acid. A duplication sometimes is characterized as a genetic region repeated one or more times (such as repeated 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 times). Duplications can range from small regions (thousands of base pairs) to whole chromosomes in some instances. Duplications frequently occur as the result of an error in homologous recombination or due to a retrotransposon event. Duplications have been associated with certain types of proliferative diseases. Duplications can be characterized using genomic microarrays or comparative genetic hybridization (CGH).

[0068] A genetic variation is sometimes an insertion. An insertion is sometimes the addition of one or more nucleotide base pairs into a nucleic acid sequence. An insertion is sometimes a microinsertion. In certain embodiments an insertion comprises the addition of a segment of a chromosome into a genome, chromosome, or segment thereof. In certain embodiments an insertion comprises the addition of an allele, a gene, an intron, an exon, any non-coding region, any coding region, segment thereof or combination thereof into a genome or segment thereof. In certain embodiments an insertion comprises the addition (i.e., insertion)of nucleic acid of unknown origin into a genome, chromosome, or segment thereof. In certain embodiments an insertion comprises the addition (i.e., insertion) of a single base.

[0069] A genetic variation sometimes includes copy number variations, i.e., variations in the number of copies of a nucleic acid sequence present in a test sample in comparison with the copy number of the nucleic acid sequence present in a reference sample. In certain embodiments, the nucleic acid sequence is 1 kb or larger. In some cases, the nucleic acid sequence is a whole chromosome or significant portion thereof. A copy number variant may refer to the sequence of nucleic acid in which copy-number differences are found by comparison of a nucleic acid sequence of interest in test sample with an expected level of the nucleic acid sequence of interest. For example, the level of the nucleic acid sequence of interest in the test sample is compared to that present in a qualified sample. Copy number variants / variations may include deletions, including microdeletions, insertions, including microinsertions, duplications, multiplications, and translocations. CNVs encompass chromosomal aneuploidies and partial aneuploidies.

[0070] “Phasing” refers to analyzing linkage information between sequence reads of a nucleic acid to determine whether two subsequences of a nucleic acid (such as alleles or variants) are located on a single chromosome or two separate chromosomes (for example, a maternally or paternally inherited chromosome).

[0071] As used herein, a “file” includes digital files. In some embodiments, a file is on a computer storage medium (such as a computer hard drive, for example a spinning magnetic disk drive or a solid state drive). In some embodiments, the digital file is stored in the format of a BAM, FASTQ, SAM, CRAM, JSON, CIGAR, or VCF file.

[0072] A “RCCX module” refers to a segmental duplication region within the RCCX region of a genome. For example, a RCCX region may include two RCCX modules. As illustrated in FIG.1, a first RCCX module may include one or more of the STK19 gene, the C4A gene, the CYP21A1P pseudogene, and the TNXA pseudogene, and a second RCCX module may include one or more of the STK19P pseudogene, the C4B gene, the CYP21A2 gene, and the TNXB gene. A RCCX module copy number that is less than 2 (such as 1) or greater than 3 (such as 3, 4, 5, etc.) may, in some embodiments, be indicative of a structural variant.Embodiments of Methods and Systems for TNXB Copy Number Estimation

[0073] FIG. 2A is a flow diagram that schematically illustrates an exemplary method 200 of estimating a copy number of a TNXB gene located within a RCCX region of a human genome. In some embodiments, the method 200 is implemented on a computer. The method 200 may be embodied in a set of executable program instructions stored on a computer- readable medium, such as one or more disk drives, of a computing system. For example, the server device 3102 shown in FIGS. 3A and 3B and described in greater detail below can execute a set of executable program instructions to implement the method 200. When the method 200 is initiated, the executable program instructions can be loaded into a memory, such as RAM, and executed by one or more processors of a server device 3102. Although the method 200 is described with respect to the server device 3102 shown in FIG. 3B, the description is illustrative only and is not intended to be limiting. In some embodiments, the method 200 and / or the method 2000 or portions thereof may be performed serially or in parallel by multiple computing systems. Receiving Sequence Reads which Align to a RCCX Region

[0074] In some embodiments, the method 200 for estimating a copy number of a TNXB gene located within a RCCX region of a human genome may begin from start block 210.

[0075] The method 200 may proceed to block 220, wherein sequence reads which align to a RCCX region of a human genome in the nucleic acid sample are received. For example, in some embodiments, the methods and systems disclosed herein include a step at block 220 of receiving a plurality of sequence reads generated from a sample obtained from a subject.

[0076] In some embodiments, the RCCX region includes two RCCX modules. For example, sequence reads which align to either of the two RCCX modules may be received. For example, the RCCX modules have nearly identical sequences. In some embodiments, each RCCX module is about 10 kb, about 15 kb, about 20 kb, about 25 kb, about 30kb (or a range constructed from any of these values) in length. In some embodiments, each RCCX module is about 20 kb in length. In some embodiments, each RCCX module is separated by about 5 kb, about 6 kb, about 7 kb, about 8 kb, about 9 kb, about 10 kb, about 11 kb, about 12 kb, about13 kb, about 14 kb, about 15 kb, about 16 kb, about 17 kb, about 18 kb, about 19 kb, about 20 kb, about 25 kb, about 30 kb (or a range constructed from any of these values). In some embodiments, each RCCX module is separated by about 13 kb.

[0077] In some embodiments, the first RCCX module includes a subsection of a STK19 gene (such as the subsection which is homologous to the STK19P pseudogene, a C4A gene, a CYP21A1P gene, and a TNXA pseudogene. In some embodiments, the second RCCX module includes a STK19P pseudogene, a C4B gene, a CYP21A2 gene, and an end of a TNXB gene. In some embodiments, the first RCCX module includes a HERV-K retrotransposon insertion in a C4A gene. In some embodiments, the second RCCX module includes a HERV- K retrotransposon insertion in a C4B gene. In some embodiments, the HERV-K retrotransposon insertion is about 6.4 kb in length. In some embodiments, the first RCCX module covers the site of a 120 bp deletion in TNXA pseudogene as compared to TNXB gene.

[0078] In some embodiments, the RCCX region includes a region corresponding to positions chr6:32024461-chr6:32043719 of reference genome hg38 and / or chr6:31991723- chr6: 32010985 of reference genome hg38. In some embodiments, the RCCX region includes a region corresponding to positions chr6:31992238-chr6:32011496 of reference genome hg19, and / or chr6:31959500-chr6:31978762 of reference genome hg19. For example, in some embodiments, the RCCX region includes a region corresponding to positions chr6:32024461- chr6:32043719 and chr6:31991723-chr6:32010985 of reference genome hg38. In some embodiments, the RCCX region includes a region corresponding to positions chr6:31992238- chr6: 32011496 and chr6:31959500-chr6:31978762 of reference genome hg19.

[0079] Sequence reads can be generated by techniques such as sequencing by synthesis, sequencing by binding, or sequencing by ligation. Sequence reads can be generated using instruments such as MINISEQ, MISEQ, NEXTSEQ, HISEQ, and NOVASEQ sequencing instruments from Illumina, Inc. (San Diego, CA). Sequence reads can be, for example, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 400, 400, 500, 600, 700, 800, 900, 1000, 1250, 1500, 1750, 2000, or more base pairs (bps) in length each. For example, sequence reads are about 100 base pairs to about 1000 base pairs in length each. The sequence reads can comprise paired-end sequence reads. The sequence reads can comprise single-end sequence reads. The sequence reads can be generated by whole genome sequencing (WGS). The WGS can be clinical WGS (cWGS). The sample can comprise cells, cell-freeDNA, cell-free fetal DNA, amniotic fluid, a blood sample, a biopsy sample, or a combination thereof. Sequence reads may comprise nucleic acid sequence information stored in a memory, and which were originally obtained from a source such as one or more biological fluids, cells, tissues, organs, or organisms.

[0080] Sequence reads may be aligned to a reference genome, such as over the RCCX region. In some embodiments, at block 220, a plurality of sequence reads are aligned to the RCCX region of a reference sequence. In some embodiments, a first plurality of sequence reads generated from a sample are aligned to a reference genome sequence to obtain a second plurality of sequence reads which align to the RCCX region in the reference genome sequence. In some embodiments, a computing system stores the first plurality of sequence reads in memory. The computing system may load the first plurality of sequence reads into memory. A sequence read can be aligned to the RCCX region in the reference sequence with an alignment quality score of zero or more. A sequence read can be aligned to either copy of the RCCX module in the reference sequence with an alignment quality score of about zero (for example, when a sequence is aligned to a region where the gene and the gene paralog are highly homologous) or more. In some embodiments, the sequence reads are obtained from a digital file containing sequencing information. Estimating a Copy Number of the RCCX Modules

[0081] The method 200 may next proceed to process block 230, wherein a copy number of RCCX modules is estimated. This process is further described in FIG. 2B. For example, the copy number of RCCX modules may be 0, 1, 2, 3, 4, 5, 6, 7, 8, or more. Counting Sequence Reads which Include a TNXA-Specific Base and Counting Sequence Reads which Include a TNXB-Specific Base

[0082] After the process block 230, the method 200 may next proceed to block 240, wherein sequence reads which include a TNXA-specific base are counted. For example, block 240 may include counting sequence reads which align to TNXA or TNXB and which include a TNXA-specific base at one or more pre-determined differentiating sites of the TNXB gene and the TNXA pseudogene, thereby obtaining a TNXA sequence read count. Next, the method 200 may next proceed to block 250, wherein sequence reads which include a TNXB-specific baseare counted. For example, block 250 may include counting sequence reads which align to TNXA or TNXB and which include a TNXB-specific base at the one or more pre-determined differentiating sites to obtain a TNXB sequence read count.

[0083] In some embodiments, block 240 and / or block 250, involve counting sequence reads at one or more pre-determined differentiating sites. In some embodiments, the pre-determined differentiating sites include sites where, if the sequence of the TNXB gene and the sequence of the TNXA pseudogene are aligned to one another, the sequence of the TNXB gene and the TNXA pseudogene differ. In some embodiments, the sequence of the TNXB gene and the TNXA pseudogene differ at the pre-determined differentiating site in at least 90%, at least 95%, at least 97%, at least 98%, or at least 99% of a population of nucleic acid samples.

[0084] In some embodiments, the one or more of pre-determined differentiating sites comprise a site corresponding to a specific genomic coordinate selected from of the TNXB gene or a corresponding position in the TNXA pseudogene on chromosome 6, in reference genome hg38.

[0085] Examples of pre-determined differentiating sites for TNXA / TNXB are given in Table A below: Table A Chromosome TNXB_position TNXB_allele TNXA_position TNXA_allele chr6 32046056 G 32013202 C

[0086] A pre-determined differentiating site may be determined as further described below. In some embodiments, the method comprises identifying single-base differences between the sequence of a first gene (such as TNXB) and a paralog gene (such as TNXA) in a reference sequence. For example, a reference sequence of the first gene may be compared with a reference sequence of a paralog gene by aligning the sequences to each other and noting all sites with single base differences between the two gene sequences. The genomic coordinates of those differentiating sites in both the first and the paralog genes may then be stored to an electronic storage. For example, a digital file may be created including a list of the single base differences. In some embodiments, the method includes selecting, as differentiating sites, single-base differences which are fixed across a population. For example, the method may include, for a plurality of nucleic acid samples (such as a plurality of nucleic acid samplesfrom a population of individuals), receiving a plurality of sequence reads which align to the first gene and the paralog gene. In some embodiments, the plurality of nucleic acid samples are derived from individuals of a population, such as more than 100, more than 500, more than 1,000, more than 5,000, or more than 10,000 individuals. In some embodiments, the plurality of samples are taken from the 1000 Genomes Project. In some embodiments, the population is a diverse population, such as a genetically diverse population including individuals from a plurality of ethnic groups, such as to account for differences in population types and increase the likelihood that single-base differences do not comprise differences due to population type. The method may further include, for each of the plurality of nucleic acid samples, estimating a gene-specific copy number for the first gene and a copy number for the paralog gene. The method may further include selecting a subset of nucleic acid samples of the plurality of nucleic acid samples, wherein the subset of nucleic acid samples comprises nucleic acid samples which are estimated to be diploid for the first gene and diploid for the paralog gene (such as using only the data from samples which are estimated to not contain a recombination event between the first gene and the paralog gene). The method may further include selecting single-base differences which have copy numbers consistent with diploidy for the first gene and the paralog gene in at least 90%, at least 95%, at least 97%, at least 98%, or at least 99% of the nucleic acid samples of the subset of nucleic acid samples.

[0087] In some embodiments, at block 240 and / or block 250, sequence information (such as a nucleotide base) is assessed at one or more pre-determined differentiating sites. For example, sequence reads which include a TNXB-specific base at a first pre-determined differentiating site among the one or more pre-determined differentiating sites may be counted at block 240, and sequence reads which include a TNXA-specific base at the first pre- determined differentiating site may be counted in block 250.

[0088] After counting sequence reads which include a TNXA-specific base and sequence reads which include a TNXB-specific base at one or more pre-determined differentiating sites, the method 200 may proceed to decision state 260, wherein the system may decide whether there are any additional pre-determined differentiating sites to be analyzed. If there is an additional pre-determined differentiating site, the method 200 may return to block 240 and the method 200 may proceed for the additional pre-determined differentiating site as previously described. If there are no additional pre-determineddifferentiating sites to be analyzed, the method 200 may proceed to block 270 as further described below. Estimating a Copy Number of the TNXB Gene

[0089] The method 200 may proceed to block 270, wherein a copy number of the TNXB gene is estimated.

[0090] At block 270, the copy number of the TNXB gene may be estimated based on the estimated copy number of RCCX modules, the TNXB sequence read count, and the TNXA sequence read count. In some embodiments, at block 270, the methods and systems assess the proportion of sequence reads supporting a TNXB-specific base at one or more pre- determined differentiating sites as compared to the total number of sequence reads covering the one or more pre-determined differentiating sites. In some embodiments, at block 270, the methods and systems assess the proportion of sequence reads supporting a TNXA-specific base at one or more pre-determined differentiating sites as compared to the total number of sequence reads covering the one or more pre-determined differentiating sites.

[0091] In some embodiments, at block 270, a probability of a TNXB copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules is calculated based on the TNXB sequence read count, the TNXA sequence read count and the estimated copy number of RCCX modules. For example, a probability of a TNXB copy number of each integer from 0 up to the integer corresponding to the estimated copy number of RCCX modules may be calculated. For example, at block 270, if the estimated copy number of RCCX modules is 4, the system may calculate a probability of a TNXB copy number of 0 (such as based on the TNXB sequence read count, the TNXA sequence read count, and ). The system may then calculate a probability of a TNXB copy number of 1 (based on the TNXB sequence read count, the TNXA sequence read count, and the estimated copy number of RCCX modules). The system may then iteratively calculate the probability of further TNXB copy numbers 2, then 3, and finally 4 (the estimated copy number of RCCX modules), each based on the TNXB sequence read count, the TNXA sequence read count, and the estimated copy number of RCCX modules.

[0092] In some embodiments, at block 270, a probability of a TNXB copy number is calculated based on a prior probability of a TNXB copy number of 0 up to a numbercorresponding to the estimated copy number of RCCX modules. Thus, in some embodiments, the estimated copy number of the TNXB gene is calculated based on a prior probability of the copy number being any individual number ranging from 0 up to the estimated copy number of RCCX modules. In some embodiments, at block 270, a Poisson posterior probability is calculated to estimate the copy number of the TNXB gene.

[0093] The methods and systems may determine the TNXB copy number with the highest calculated probability. The methods and systems may report and / or display the TNXB copy number with the highest calculated probability. In some embodiments, the methods and systems disclosed herein further include a step of creating a digital file including an estimated TNXB copy number. In some embodiments, the digital file further includes an estimated copy number for the RCCX modules, and / or an estimated TNXA copy number.

[0094] Optionally, the method 200 may include one or more quality control steps (not illustrated). In some embodiments, one or more quality control steps includes one or more steps of checking for missing and / or zero coverage. In some embodiments, the one or more quality control steps are performed prior to block 270. For example, in some embodiments, the methods and systems output a no-call if the estimated non-integer copy number is more than a predetermined threshold away from an integer copy number. For example, if a raw copy number estimate for a given differentiating site is 0.4 (between copy number 0 and 1), the methods and systems may make a determination to not make a copy number call instead of rounding to the nearest integer number, resulting in a null output. In some embodiments, one or more quality control steps include determining if the estimated copy number of RCCX modules is missing. In some embodiments, if the estimated copy number of RCCX modules is missing, the systems and methods do not estimate a copy number of the TNXB gene. In some embodiments, the one or more quality control steps include determining if the estimated copy number of RCCX modules is 0. In some embodiments, if the estimated copy number of RCCX modules is 0, the systems and methods estimate a copy number of 0 for the TNXB gene.

[0095] The method 200 may end at block 280.

[0096] Similarly, the methods and systems disclosed herein may estimate a copy number of the TNXA pseudogene using methods and systems similar to those described above. For example, the copy number of the TNXA pseudogene may be estimated based on theestimated copy number of RCCX modules, the TNXB sequence read count, and the TNXA sequence read count, as described above. Processes of Estimating a Copy Number of RCCX Modules

[0097] FIG.2B is a block diagram that further illustrates the methods taking place within the process block 230 described above, wherein a copy number of RCCX modules is estimated. As shown in FIG. 2B, the method of process block 230 may start from start block 2310.

[0098] In some embodiments, the method of process block 230 may proceed to block 2320, wherein sequence reads which align to an RCCX module of the RCCX region are counted. In some embodiments, at block 2320, the methods and systems count sequence reads which align to a RCCX module within the RCCX region of a human genome. For example, sequence reads may have been previously aligned to a reference sequence as described. In some embodiments, estimating a copy number of the RCCX modules of the human genome comprises counting sequence reads which align to a C4A gene, a CYP21A1P gene, a TNXA pseudogene, a C4B gene, a CYP21A2 gene, and / or a TNXB gene in the human genome. In some embodiments, sequence reads are counted which align to either of the two RCCX modules (such as either copy of a gene within the RCCX region).

[0099] In some embodiments, sequence reads may be counted which align to a subsection of a RCCX module used for copy number estimation. For example, in some embodiments, estimating a copy number of the RCCX modules of the human genome comprises counting sequence reads which align to a region corresponding to positions chr6: 32024461-chr6:32043719 of reference genome hg38, chr6: 31991723-chr6: 32010985 of reference genome hg38, chr6:31992238-chr6: 32011496 of reference genome hg19, and / or chr6:31959500-chr6:31978762 of reference genome hg19. In some embodiments, sequence reads are counted if they align to one or more sites within the aforementioned positions.

[0100] The method of process block 230 may next proceed to block 2330, wherein the count of sequence reads which align to a RCCX module is normalized with diploid sequence reads. In some embodiments, block 2330 includes normalizing the count of the sequence reads which align to a module of the RCCX region of the human genome. In some embodiments, the sequence read count is normalized by the length of a RCCX module (or asubsection of a RCCX module). In some embodiments, the sequence read count may be normalized by the length of the RCCX module (or a subsection of a RCCX module) and against a set diploid sequence reads. In some embodiments, the diploid sequence reads are obtained from regions of a human genome that are expected or observed to consistently be diploid across populations. For example, a diploid region may be a region which is diploid in over 90%, over 95%, or over 99% of nucleic acid samples from a population.

[0101] In some embodiments, the method 200 includes a step (not shown) of counting sequence reads which align to diploid regions in the human genome within the nucleic acid sample. For example, sequence read obtained from the human genome within the nucleic acid sample can be obtained and aligned as previously described herein to regions of a reference sequence previously designated as diploid regions. Sequence reads which align to one or more diploid regions may be counted.

[0102] In some embodiments, the diploid sequence reads are obtained from about 3,000 genomic regions of approximately 2000 bp in length, which are expected to be consistently diploid across populations. In some embodiments, block 2330 includes normalization using (1a) a depth of the sequence reads aligned to the RCCX module (or a subsection of a RCCX module), (1b) a length of each RCCX module (or subsection of the RCCX module), (2a) a depth of sequence reads aligned to diploid regions, and (2b) a length of each of the diploid regions.

[0103] In some embodiments, block 2330 includes a step of GC-correcting the sequence read count. For example, in some embodiments, the sequence read count (for example, a sequence read count normalized by length of the region) for the RCCX module (or subsection of a module) is pooled together with sequence read counts (for example, a sequence read count normalized by length of the region) for diploid regions. Normalizing the count of sequence reads which align to the RCCX module (or subsection of a RCCX module) by the count of sequence reads which align to diploid regions may, in some embodiments, correct for bias in sequencing coverage due to variable GC content among different regions. For example, the count of sequence reads aligned to the RCCX module may be corrected for GC content using (1) a GC content of each of the RCCX module (or subsection of an RCCX module) and (2) a GC content of each of diploid regions. In some embodiments, in block 2330, a normalized and / or GC-corrected copy number is determined for the RCCX module. In some embodiments,the normalized and / or GC-corrected copy number is a float copy number, including a non- integer number such as 1.2, 2.4, etc.

[0104] The method of process block 230 may next proceed to block 2340, wherein a Gaussian mixture model is applied. In some embodiments, block 2340 includes binning the normalized and / or GC-corrected count of the sequence reads which align to the RCCX module (or subsection of the RCCX module) of the human genome using a Gaussian mixture model (GMM). For example, a normalized and / or GC-corrected count of sequence reads may be a non-integer number such as 3.4, 3.2, etc., and a Gaussian mixture model may be used to determine an integer copy number such as “3.” For example, a Gaussian mixture model may be used to infer the most likely copy number of RCCX modules based on the observed normalized depth signal.

[0105] The total copy number can be, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more copies. The Gaussian mixture model can comprise a one-dimensional Gaussian mixture model. The plurality of Gaussians of the Gaussian mixture model can represent integer copy numbers, for example, 0 to 5, 0 to 6, 0 to 7, 0 to 8, 0 to 9, 0 to 10, 0 to 11, 0 to 12, 0 to 13, 0 to 14, or 0 to 15. For example, the plurality of Gaussians of the Gaussian mixture model can represent integer copy numbers from 0 to 10. A mean of each of the plurality of Gaussians can be the integer copy number represented by the Gaussian. A mean of each of the plurality of Gaussians can be the integer copy number represented by the Gaussian (such as copy numbers of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or more). The standard deviation of a Gaussian can be or be about, for example, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, or more. The plurality of Gaussians of the Gaussian mixture model can comprise, for example, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or more, Gaussians. For example, the plurality of Gaussians of the Gaussian mixture model can comprise 5 Gaussians. In some embodiments, the GMM includes pre- defined parameters such as shift, prior, mean, and standard deviation (sd). In some embodiments, a normalized and GC-corrected depth is first scaled by a shift value that corrects for alignment bias between RCCX module and diploid regions.

[0106] At block 2340, the methods and systems may determine an estimated copy number of RCCX modules. In some embodiments, the posterior probability of CN = i given scaled depth is computed for i = 0- X (X being a maximum possible copy number) based on the pre-trained mean, sd, and prior values from the Gaussian mixture model. In someembodiments, the integer copy number with highest posterior probability is then selected as candidate for the estimated copy number of RCCX modules. The computing system can determine the estimated copy number using the Gaussian mixture model and a predetermined posterior probability threshold, given the normalized number of the sequence reads aligned to the RCCX region. The predetermined posterior probability threshold can be, for example, 0.7, 0.75, 0.8, 0.85, 0.95, or more. In some embodiments, the predetermined posterior probability threshold is 0.95. In some embodiments, the estimated copy number of RCCX modules is an integer copy number.

[0107] The method of process block 230 may end at block 2350. Embodiments of Methods and Systems for C4A / C4B Copy Number Estimation

[0108] FIG. 2C is a block diagram that schematically illustrates an exemplary method 2000 of estimating a copy number of a C4A gene or a C4B gene located within a RCCX region of a human genome. In some embodiments, the method 2000 is implemented on a computer. The method 2000 may be embodied in a set of executable program instructions stored on a computer-readable medium, such as one or more disk drives, of a computing system. For example, the server device 3102 shown in FIGS. 3A and 3B and described in greater detail below can execute a set of executable program instructions to implement the method 2000. When the method 2000 is initiated, the executable program instructions can be loaded into a memory, such as RAM, and executed by one or more processors of a server device 3102. Although the method 2000 is described with respect to the server device 3102 shown in FIG. 3B, the description is illustrative only and is not intended to be limiting. In some embodiments, the method 2000 or portions thereof may be performed serially or in parallel by multiple computing systems.

[0109] The method 2000 for estimating a copy number of a C4A gene or a C4B gene located within a RCCX region of a human genome may begin from start block 2010. In some embodiments, the method 2000 may proceed to block 2020, wherein sequence reads which align to a RCCX region of a human genome in the nucleic acid sample are received. In some embodiments, this is performed similar to the step of block 220 from FIG.2A described above.

[0110] The method 2000 may next proceed to process block 2030, wherein a copy number of RCCX modules is estimated. In some embodiments, this is performed similar to the process of process block 230 from FIG. 2A described above (and as further described in FIG. 2B). Counting Sequence Reads which Include a C4A-Specific Base and Counting Sequence Reads which Include a C4B-Specific Base

[0111] After the process block 2030, the method 2000 may next proceed to block 2040, wherein sequence reads which include a C4A-specific base are counted. For example, block 2040 may include counting sequence reads which align to C4A or C4B and which include a C4A-specific base at one or more pre-determined differentiating sites of a C4A gene and a C4B gene, thereby obtaining a C4A sequence read count. In some embodiments, this is performed similar to the process of process block 240 from FIG. 2A described above), except using sequence reads which align to C4A or C4B, and using C4A and C4B-specific bases at one or more pre-determined differentiating sites of the C4A gene and the C4B gene as further described below.

[0112] Next, the method 2000 may next proceed to block 2050, wherein sequence reads which include a C4B-specific base are counted. For example, block 2050 may include counting sequence reads which align to C4A or C4B and which include a C4B-specific base at the one or more pre-determined differentiating sites to obtain a C4B sequence read count. In some embodiments, this is performed similar to the process of process block 250 from FIG. 2A described above), except using sequence reads which align to C4A or C4B, and using C4A and C4B-specific bases at one or more pre-determined differentiating sites of the C4A gene and the C4B gene as further described below.

[0113] In some embodiments, block 2040 and / or block 2050 involve counting sequence reads at one or more pre-determined differentiating sites. In some embodiments, the pre-determined differentiating sites include sites where the sequence of the C4A gene and the C4B gene differ. In some embodiments, the sequence of the C4A gene and the C4B pseudogene differ at the pre-determined differentiating site in at least 90%, at least 95%, at least 97%, at least 98%, or at least 99% of a population of nucleic acid samples.

[0114] In some embodiments, the one or more pre-determined differentiating sites comprise a site corresponding to a specific genomic coordinate of the C4A gene or a corresponding position in the C4B gene, in reference genome hg38. Pre-determined differentiating sites may be determined using methods described above with reference to the method 200.

[0115] Examples of pre-determined differentiating sites for C4A / C4B are given in Table B below: Table B Chromosome C4B_Position C4B_Allele C4A_Position C4A_Allele chr6 32028821 T 31996083 C chr6 32028824 C 31996086 G chr6 32028832 A 31996094 T chr6 32028835 C 31996097 G chr6 32028837 T 31996099 C

[0116] After counting sequence reads which include a C4A-specific base and sequence reads which include a C4B-specific base at one or more pre-determined differentiating sites, the method 2000 may proceed to decision state 2060, wherein the system may decide whether there are any additional pre-determined differentiating sites to be analyzed. If there is an additional pre-determined differentiating site, the method 2000 may return to block 2040 and the method 2000 may proceed for the additional pre-determined differentiating site as previously described. If there are no additional pre-determined differentiating sites to be analyzed, the method 2000 may proceed to block 2070 as further described below. Estimating a Copy Number of the C4A Gene or the C4B Gene

[0117] The method 2000 may proceed to block 2070, wherein a copy number of the C4A gene or the copy number of the C4B gene is estimated. For example, in the method 2000, at block 2070, the copy number of the C4A gene or the copy number of the C4B gene may be estimated based on the estimated copy number of RCCX modules, the C4A sequence read count, and the C4B sequence read count. The methods and systems may perform steps atblock 2070 similar to those disclosed above with respect to block 270 and the method 200, except using sequence reads which align to C4A or C4B, and using C4A and C4B-specific bases at one or more pre-determined differentiating sites of the C4A gene and the C4B gene.

[0118] The methods and systems disclosed herein may also estimate a combined C4 copy number by estimating a copy number of the C4A gene as described herein, and estimating a copy number of the C4B gene as described herein. For example, if the estimated copy number of the C4A gene is 1, and the estimated copy number of the C4B gene is 2, the estimated combined C4 copy number may be 3.

[0119] The method 2000 may end at block 2080. Methods of Detecting Variants in TNXA / TNXB

[0120] In another aspect, disclosed herein are methods and systems of detecting one or more single-nucleotide variants or indels in a TNXB gene or a TNXA pseudogene in a nucleic acid sample.

[0121] In some embodiments, the methods and systems determine sequence reads from the nucleic acid sample. For example, sequence reads may be determined as previously described herein with reference to the method 200 and / or the method 2000.

[0122] In some embodiments, the methods and systems obtain sequence reads which align to a site of a single-nucleotide variant or indel (collectively, “small variant”) in a TNXB gene or a TNXA pseudogene of a human genome in the nucleic acid sample. For example, sequence reads may be aligned to a reference genome as previously described herein with reference to the method 200 and / or the method 2000.

[0123] Sequence reads may be collected which align to either the TNXB gene or the TNXA pseudogene in the reference sequence over the site of the single-nucleotide variant or indel in the TNXB gene or the corresponding site in the TNXA pseudogene. In some embodiments, sequence reads with low or zero mapping quality are included in the alignment. For example, if the site of the single-nucleotide variant or indel is in the TNXB gene, sequence reads may be obtained which align to the site of the single-nucleotide variant or indel in the TNXB gene in a human genome of the nucleic acid sample. Sequence reads may also be obtained which align to a corresponding site of the single-nucleotide variant or indel in a TNXA pseudogene in human genome of the nucleic acid sample. In some embodiments, alignment ofreads with low or zero mapping quality (for example, sequence reads which align ambiguously to the TNXB gene or the TNXA pseudogene) allow for more accurate small variant detection as a greater number of sequence reads may be used to detect the small variant. For example, in some prior art methods, sequence reads with low or zero mapping quality are filtered out and are not used in determining the sequence of a region or detecting variants in a region. The systems and methods described herein may, in some embodiments, advantageously use sequence reads with low or zero mapping quality to detect small variants without determining whether the small variant is located in the TNXB gene or the TNXA pseudogene. In some embodiments, this may allow for an initial detection of a small variant in a region in which, due to high sequence similarity between the TNXB gene or the TNXA pseudogene, it may be difficult to otherwise detect small variants in either the TNXB gene or the TNXA pseudogene.

[0124] In some embodiments, the sequence reads are derived from short-read sequencing systems or processes. In some embodiments, the short-read sequence reads are about 75 bp to about 500 bp in length. In other embodiments, the short-read sequence reads are 200 bp to about 400 bp in length. In some embodiments the sequence reads are less than 400 bp in length. In some embodiments the sequence reads are less than 1,000 bp in length.

[0125] In some embodiments, the methods and systems count sequence reads which contain a base corresponding to an alternative allele at the site of the single-nucleotide variant or indel. In some embodiments, counting sequence reads comprises counting both sequence reads which align to the TNXB gene (and which include the site of the single- nucleotide variant or indel) and sequence reads which align to the TNXA pseudogene (and which include the site of the single-nucleotide variant or indel). In some embodiments, the sequence read count may be normalized and GC-corrected as previously described herein with reference to the method 200 and / or method 2000. In some embodiments, an integer copy number is estimated for an alternative or variant allele based on a) a combined count of sequence reads covering corresponding positions of the small variant in the TNXB gene and the TNXA pseudogene, b) a count of sequence reads supporting reference alleles, and c) a count of sequence reads supporting alternative alleles.

[0126] In some embodiments, the methods and systems count sequence reads which contain a base corresponding to a reference allele at the site of the single-nucleotide variant or indel. In some embodiments, counting sequence reads comprises counting bothsequence reads which align to the TNXB gene (and which include the site of the single- nucleotide variant or indel) and sequence reads which align to the TNXA pseudogene (and which include the site of the single-nucleotide variant or indel). In some embodiments, the sequence read count may be normalized and GC-corrected as previously described herein with reference to the method 200 and / or method 2000. In some embodiments, an integer copy number is estimated for a reference allele based on a) a combined count of sequence reads covering corresponding positions of the small variant in the TNXB gene and the TNXA pseudogene, b) a count of sequence reads supporting reference alleles, and c) a count of sequence reads supporting alternative alleles.

[0127] In some embodiments, the methods and systems detect the single-nucleotide variant or indel based on the count of the sequence reads which contain a base corresponding to an alternative allele and based on the count of sequence reads which contain a base corresponding to a reference allele. For example, in some embodiments, the single-nucleotide variant or indel will be reported if a significant portion of sequence reads support the alternative allele. For example, the small variant may be reported if about 10% or more, about 20% or more, about 30% or more, about 40% or more, about 50% or more, about 60% or more, about 70% or more, or about 80% or more, or about 90% or more sequence reads which cover the small variant contain a base corresponding to an alternative allele at the site of the small variant, as compared to a reference allele at the site. In some embodiments, the small variant may be reported if one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or ten or more sequence reads contain an alternative allele at the site of the variant.

[0128] In some embodiments, the variant call is not specific to the TNXB gene or the TNXA pseudogene. For example, in some embodiments, the variant call is not assigned to the TNXB gene or to the TNXA pseudogene. In some embodiments, the variant call is not phased. In some embodiments, a small variant may be farther than one sequence read length (such as farther than 100 bp, 150 bp, 200 bp, 250 bp, 400 bp, 450 bp, or more) away from the one or more target regions described herein. In some embodiments, making a variant call ambiguous to the TNXB gene or the TNXA pseudogene advantageously allows a user to detect one or more single-nucleotide variants or indels in a RCCX region in a nucleic acid sample while more efficiently using computing power and memory, as a detected small variant doesnot need to be phased into a candidate haplotype, and the methods and systems do not require that sequence reads are further analyzed to determine whether a small variant is assigned to the TNXB gene or the TNXA pseudogene. In some embodiments, detecting a small variant in region-ambiguous manner improves computational resource efficiency and enables high precision and recall on discovering the variant allele, as compared to de-novo small variant calling or calling a small variant and phasing the small variant into a region or a haplotype, which require a much more complex process, are much less computationally efficient, and potentially provide less precision or recall for the variant of interest.

[0129] In some embodiments, a variant call that is ambiguous to the TNXB gene or the TNXA pseudogene advantageously allows a user to detect a small variant using short-read sequencing. In some embodiments, short-read sequencing reads (such as sequence reads that include about 75-500 bp) over the TNXB gene or the TNXA pseudogene do not contain enough information to uniquely place the small variant and the user does not necessarily need to know the unique placement of the variant. In some embodiments, an advantage of making a region- ambiguous call is that the user avoids the need to initially perform more extensive sequencing assays, such as long-read sequencing assays. The information required can be obtained from the same whole genome sequencing (WGS) assay used to perform variant calling on the rest of the genome.

[0130] In some embodiments, once a variant call ambiguous to the TNXB gene or the TNXA pseudogene has been made, the placement of the single-nucleotide variant or indel in the TNXB gene or the TNXA pseudogene can be confirmed with orthogonal (long-read) sequencing methods known to those of skill in the art. For example, after a single-nucleotide variant or indel is detected in a manner not specific to the TNXB gene or the TNXA pseudogene, additional sequencing such as orthogonal techniques are used to confirm the variant call and / or phase the variant into regions.

[0131] In some embodiments, the single-nucleotide variant or indel includes TNXB_c.12220C>T and / or TNXB_c.10623_10624del.

[0132] In some embodiments, the methods and systems create a digital file including a variant call corresponding to the single-nucleotide variant or indel. In some embodiments, the file includes, for each single-nucleotide variant or indel, a reference for the small variant, a count of sequence reads supporting an alternative allele, and a count ofsequence reads supporting a reference allele. In some embodiments, the digital file also includes estimated copy number information as described herein. Methods of Detecting Variants in C4A / C4B

[0133] In another aspect, disclosed herein are methods and systems of detecting one or more single-nucleotide variants or indels in a C4A gene or a C4B gene in a nucleic acid sample.

[0134] In some embodiments, the methods and systems determine sequence reads from the nucleic acid sample. For example, sequence reads may be determined as previously described herein with reference to the method 200 and / or the method 2000.

[0135] In some embodiments, the methods and systems obtain sequence reads which align to a site of a single-nucleotide variant or indel (collectively, “small variant”) in a C4A gene or a C4B gene of a human genome in the nucleic acid sample. In some embodiments, the methods and systems obtaining sequence reads which align to a corresponding site of the single-nucleotide variant or indel in a paralog C4A gene or a paralog C4B gene in human genome of the nucleic acid sample. For example, if the small variant is in the C4A gene, sequence reads may be obtained which align to the site of the small variant in the C4A gene, and sequence reads may be obtained which align to a corresponding site of the small variant in the paralog C4B gene. In some embodiments, these steps are performed similar to the steps of obtaining sequence reads from the methods and systems of detecting one or more single- nucleotide variants or indels in a TNXB gene or a TNXA pseudogene in a nucleic acid sample.

[0136] In some embodiments, the methods and systems count sequence reads which contain a base corresponding to an alternative allele at the site of the single-nucleotide variant or indel. In some embodiments, the methods and systems count sequence reads which contain a base corresponding to a reference allele at the site of the single-nucleotide variant or indel. In some embodiments, these steps are performed similar to the steps of counting sequence reads from the methods and systems of detecting one or more single-nucleotide variants or indels in a TNXB gene or a TNXA pseudogene in a nucleic acid sample, except using sequence reads which align to C4A or C4B, and using single-nucleotide variants or indels (and their associated sites) of the C4A gene and the C4B gene. For example, sequence reads whichalign to the C4A gene and sequence reads which align to the C4B gene may be counted, for each of the alternative alle and the reference allele.

[0137] In some embodiments, the single-nucleotide variant or indel includes C4A_insCT and / or C4B_3676G>A.

[0138] In some embodiments, the methods and systems detect the single-nucleotide variant or indel based on the count of the sequence reads which contain a base corresponding to an alternative allele and the count of sequence reads which contain a base corresponding to a reference allele.

[0139] In some embodiments, this is performed similar to the step of detect the single-nucleotide variant or indel based on the count of the sequence reads which contain a base corresponding to an alternative allele and the count of sequence reads which contain a base corresponding to a reference allele, from the methods and systems of detecting one or more single-nucleotide variants or indels in a TNXB gene or a TNXA pseudogene in a nucleic acid sample, except using reference / alternative allele counts based on single-nucleotide variants or indels (and their associated sites) of the C4A gene and the C4B gene and sequence reads thereof. For example, in some embodiments, the detection of the single-nucleotide variant or indel is advantageously not specific to the C4A gene or the C4B gene, as described above. Embodiments of Sequencing Systems

[0140] FIG. 3A illustrates a diagram of an environment in which a copy number detection system can operate in accordance with one or more implementations. The following paragraphs describe the copy number detection system with respect to illustrative figures that portray example implementations and embodiments. For example, FIG. 3A illustrates a schematic diagram of a computing system 3000 in which a copy number detection system 3106 operates in accordance with one or more implementations. As illustrated, the computing system 3000 includes one or more server device(s) 3102 connected to a user client device 3108, a local device 3118, and a sequencing device 3114 via a network 3112. The network 3112 can comprise any suitable network over which computing devices can communicate.

[0141] As shown in FIG. 3A, the computing system 3000 includes the server device(s) 3102. In various implementations, the server device(s) 3102 may generate, receive,analyze, store, and transmit digital data, such as data for nucleobase calls or sequenced nucleic- acid polymers. In some implementations, the server device(s) 3102 receive various data from the sequencing device 3114, such as data from a sample genome and / or sequence reads. The server device(s) 3102 may also communicate with the user client device 3108. In particular, the server device(s) 3102 can send data for sequence reads, direct nucleobase calls, nucleobase calls, and / or sequencing metrics to the user client device 3108.

[0142] As shown, the server device(s) 3102 includes a sequencing application 3110. In general, the sequencing application 3110 analyzes the data (such as call data) received from the sequencing device 3114 or elsewhere to determine nucleobase sequences for nucleic- acid polymers. For example, the sequencing application 3110 can receive raw data from the sequencing device 3114 and determine a nucleobase sequence for a sample genome or a nucleic-acid segment. In some implementations, the sequencing application 3110 determines the sequences of nucleobases in DNA and / or RNA segments or oligonucleotides.

[0143] As also shown, the sequencing application 3110 includes the copy number detection system 3106. As described below, in some embodiments, the copy number detection system 3106 can estimate a copy number of a TNXB gene or a TNXA pseudogene located within a RCCX region of a human genome. For example, in some embodiments, the copy number detection system 3106 estimates a copy number of RCCX modules in a nucleic acid sample from a human genome. In some embodiments, the copy number detection system 3106 counts sequence reads which include a TNXA-specific base at one or more pre-determined differentiating sites of a TNXB gene and a TNXA pseudogene within the RCCX region to obtain a TNXA sequence read count. In some embodiments, the copy number detection system 3106 counts sequence reads which include a TNXB-specific base at the one or more pre-determined differentiating sites to obtain a TNXB sequence read count. In some embodiments, the copy number detection system 3106 estimates the copy number of the TNXB gene or the TNXA pseudogene based on the estimated copy number of RCCX modules, the TNXB sequence read count, and the TNXA sequence read count.

[0144] As described below, in some embodiments, the copy number detection system 3106 can estimate a copy number of a C4A gene or a C4B gene located within a RCCX region of a human genome. For example, in some embodiments, the copy number detection system 3106 estimates a copy number of RCCX modules in a nucleic acid sample from ahuman genome. In some embodiments, the copy number detection system 3106 counts sequence reads which include a C4A-specific base at one or more pre-determined differentiating sites of a C4A gene and a C4B gene within the RCCX region to obtain a C4A sequence read count. In some embodiments, the copy number detection system 3106 counts sequence reads which include a C4B-specific base at the one or more pre-determined differentiating sites to obtain a C4B sequence read count. In some embodiments, the copy number detection system 3106 estimates the copy number of the C4A gene or the copy number of the C4B gene based on the estimated copy number of RCCX modules, the C4A sequence read count, and the C4B sequence read count.

[0145] Moreover, while the copy number detection system 3106 is described being implemented on the server device(s) 3102, as part of the sequencing application 3110, in some implementations, the copy number detection system 3106 is implemented by (such as located entirely or in part) on the user client device 3108, the sequencing device 3114, and / or the local device 3118. As mentioned, in some implementations, the copy number detection system 3106 is implemented by one or more other components of the computing system 3000, such as the sequencing device 3114. In particular, the copy number detection system 3106 can be implemented in a variety of different ways across the server device(s) 3102, the network 3112, the user client device 3108, the local device 3118, and the sequencing device 3114.

[0146] As further shown in FIG.3A, the computing system 3000 includes the user client device 3108. In various implementations, the user client device 3108 can generate, store, receive, and send digital data. In particular, the user client device 3108 can receive the data from the sequencing device 3114. As further illustrated, the user client device 3108 includes a sequencing application 3110. The sequencing application 3110 may be a web application or a native application stored and executed on the user client device 3108 (e.g., a mobile application, desktop application, or web application). The sequencing application 3110 can receive data from the sequencing application 3110 and / or copy number detection system 3106. For example, the user client device 3108 can receive variant call files and / or alignment files from the sequencing application 3110.

[0147] The sequencing application 3110 can also include instructions that (when executed) cause the user client device 3108 to receive data from the copy number detection system 3106 and present data from the sequencing device 3114 and / or the server device(s)3102. Furthermore, the sequencing application 3110 can instruct the user client device 3108 to display data for variant calls, such as nucleobase calls or an indication of a copy number variant. Indeed, the user client device 3108 can display nucleobase call results for a genome sample and / or an indication of a predicted copy number variant.

[0148] As further shown in FIG. 3A, the computing system 3000 includes the sequencing device 3114. In various implementations, the sequencing device 3114 can sequence a genomic sample or other nucleic-acid polymer. For example, the sequencing device 3114 analyzes nucleic-acid segments or oligonucleotides extracted from genomic samples to generate data either directly or indirectly on the sequencing device 3114. More particularly, the sequencing device 3114 receives and analyzes, within nucleotide-sample slides (such as flow cells), nucleic-acid sequences extracted from genomic samples. In one or more implementations, the sequencing device 3114 utilizes SBS to sequence a genomic sample or other nucleic-acid polymers. In addition to, or in the alternative to communicating across the network 3112, in some implementations, the sequencing device 3114 bypasses the network 3112 and communicates directly with the user client device 3108.

[0149] As further depicted in FIG. 3A, in some implementations, the server device(s) 3102 includes a distributed collection of servers, where the server device(s) 3102 include several server devices distributed across the network 3112 and located in the same or different physical locations. For instance, the server device(s) 3102 can be implemented, in whole or in part, on the local device 3118. To illustrate, the local device 3118 may implement the sequencing application 3110 and / or the copy number detection system 3106. Further, the server device(s) 3102 and / or the local device 3118 can include a content server, an application server, a communication server, a web-hosting server, or another type of server.

[0150] The user client device 3108 illustrated in FIG.3A can include various types of client devices. For example, in some implementations, the user client device 3108 includes non-mobile devices, such as desktop computers or servers, or other types of client devices. In various implementations, the user client device 3108 includes mobile devices, such as laptops, tablets, mobile telephones, or smartphones.

[0151] Though FIG. 3A illustrates the components of the computing system 3000 communicating via the network 3112, in certain implementations, the components of computing system 3000 can also communicate directly with each other, bypassing the network3112. For instance, in some implementations, the user client device 3108 communicates directly with the sequencing device 3114. Additionally, in some implementations, the user client device 3108 communicates directly with the copy number detection system 3106 and / or the server device(s) 3102. In some implementations, the user client device 3108 communicates directly with the local device 3118. Moreover, the copy number detection system 3106 can access one or more databases housed on or accessed by the server device(s) 3102 or elsewhere in the computing system 3000.

[0152] FIG.3B is a block diagram of an exemplary server device 3102 that may be used in connection with the computing system 3000 of FIG. 3A. The server device 3102 may be configured to determine a copy number variant genotype in a nucleic acid sample. The general architecture of the server device 3102 depicted in FIG.3B includes an arrangement of computer hardware and software components. The server device 3102 may include many more (or fewer) elements than those shown in FIG.3B. It is not necessary, however, that all of these generally conventional elements be shown in order to provide an enabling disclosure. As illustrated, the server device 3102 includes a processing unit 310, a network interface 320, a computer readable medium drive 330, an input / output device interface 340, a display 350, and an input device 360, all of which may communicate with one another by way of a communication bus. The network interface 320 may provide connectivity to one or more networks or computing systems. The processing unit 310 may thus receive information and instructions from other computing systems or services via a network. The processing unit 310 may also communicate to and from memory 370 and further provide output information for an optional display 350 via the input / output device interface 340. The input / output device interface 340 may also accept input from the optional input device 360, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, gamepad, accelerometer, gyroscope, or other input device.

[0153] The memory 370 may contain computer program instructions (grouped as modules or components in some embodiments) that the processing unit 310 executes in order to implement one or more embodiments. The memory 370 generally includes RAM, ROM and / or other persistent, auxiliary or non-transitory computer readable media. The memory 370 may store an operating system 372 that provides computer program instructions for use by the processing unit 310 in the general administration and operation of the server device 3102. Thememory 370 may store a reference genome 373, such as for use by the sequencing application 3110. The memory 370 may further include computer program instructions and other information for implementing aspects of the present disclosure.

[0154] For example, in one embodiment, the memory 370 includes a sequencing application 3110, which may include a copy number detection system 3106. The copy number detection system 3106 can perform the methods disclosed herein. In addition, memory 370 may include or communicate with the data store 390 and / or one or more other data stores that store one or more inputs, one or more outputs, and / or one or more results (including intermediate results) of determining a copy number variant genotype in a nucleic acid sample of the present disclosure, such the sequencing reads, the estimated copy number(s), and the variant call (for example, the detection of a copy number variant) determined.

[0155] In some embodiments, the disclosed systems and methods may involve approaches for shifting or distributing certain sequence data analysis features and sequence data storage to a cloud computing environment or cloud-based network. User interaction with sequencing data, genome data, or other types of biological data may be mediated via a central hub that stores and controls access to various interactions with the data. In some embodiments, the cloud computing environment may also provide sharing of protocols, analysis methods, libraries, sequence data as well as distributed processing for sequencing, analysis, and reporting. In some embodiments, the cloud computing environment facilitates modification or annotation of sequence data by users. In some embodiments, the systems and methods may be implemented in a computer browser, on-demand or on-line.

[0156] In some embodiments, software written to perform the methods as described herein is stored in some form of computer readable medium, such as memory, CD- ROM, DVD-ROM, memory stick, flash drive, hard drive, SSD hard drive, server, mainframe storage system and the like.

[0157] In some embodiments, the methods may be written in any of various suitable programming languages, for example compiled languages such as C, C#, C++, Fortran, and Java. Other programming languages could be script languages, such as Perl, MatLab, SAS, SPSS, Python, Ruby, Pascal, Delphi, R and PHP. In some embodiments, the methods are written in C, C#, C++, Fortran, Java, Perl, R, Java or Python. In some embodiments, the method may be an independent application with data input and data displaymodules. Alternatively, the method may be a computer software product and may include classes wherein distributed objects comprise applications including computational methods as described herein.

[0158] In some embodiments, the methods may be incorporated into pre-existing data analysis software, such as that found on sequencing instruments. Software comprising computer implemented methods as described herein are installed either onto a computer system directly, or are indirectly held on a computer readable medium and loaded as needed onto a computer system. Further, the methods may be located on computers that are remote to where the data is being produced, such as software found on servers and the like that are maintained in another location relative to where the data is being produced, such as that provided by a third party service provider.

[0159] An assay instrument, desktop computer, laptop computer, or server which may contain a processor in operational communication with accessible memory comprising instructions for implementation of systems and methods. In some embodiments, a desktop computer or a laptop computer is in operational communication with one or more computer readable storage media or devices and / or outputting devices. An assay instrument, desktop computer and a laptop computer may operate under a number of different computer based operational languages, such as those utilized by Apple based computer systems or PC based computer systems. An assay instrument, desktop and / or laptop computers and / or server system may further provide a computer interface for creating or modifying experimental definitions and / or conditions, viewing data results and monitoring experimental progress. In some embodiments, an outputting device may be a graphic user interface such as a computer monitor or a computer screen, a printer, a hand-held device such as a personal digital assistant (i.e., PDA, Blackberry, iPhone), a tablet computer (such as iPAD), a hard drive, a server, a memory stick, a flash drive and the like.

[0160] A computer readable storage device or medium may be any device such as a server, a mainframe, a supercomputer, a magnetic tape system and the like. In some embodiments, a storage device may be located onsite in a location proximate to the assay instrument, for example adjacent to or in close proximity to, an assay instrument. For example, a storage device may be located in the same room, in the same building, in an adjacent building, on the same floor in a building, on different floors in a building, etc. in relation to the assayinstrument. In some embodiments, a storage device may be located off-site, or distal, to the assay instrument. For example, a storage device may be located in a different part of a city, in a different city, in a different state, in a different country, etc. relative to the assay instrument. In embodiments where a storage device is located distal to the assay instrument, communication between the assay instrument and one or more of a desktop, laptop, or server is typically via Internet connection, either wireless or by a network cable through an access point. In some embodiments, a storage device may be maintained and managed by the individual or entity directly associated with an assay instrument, whereas in other embodiments a storage device may be maintained and managed by a third party, typically at a distal location to the individual or entity associated with an assay instrument. In embodiments as described herein, an outputting device may be any device for visualizing data.

[0161] An assay instrument, desktop, laptop and / or server system may be used itself to store and / or retrieve computer implemented software programs incorporating computer code for performing and implementing computational methods as described herein, data for use in the implementation of the computational methods, and the like. One or more of an assay instrument, desktop, laptop and / or server may comprise one or more computer readable storage media for storing and / or retrieving software programs incorporating computer code for performing and implementing computational methods as described herein, data for use in the implementation of the computational methods, and the like. Computer readable storage media may include, but is not limited to, one or more of a hard drive, a SSD hard drive, a CD-ROM drive, a DVD-ROM drive, a floppy disk, a tape, a flash memory stick or card, and the like. Further, a network including the Internet may be the computer readable storage media. In some embodiments, computer readable storage media refers to computational resource storage accessible by a computer network via the Internet or a company network offered by a service provider rather than, for example, from a local desktop or laptop computer at a distal location to the assay instrument.

[0162] In some embodiments, computer readable storage media for storing and / or retrieving computer implemented software programs incorporating computer code for performing and implementing computational methods as described herein, data for use in the implementation of the computational methods, and the like, is operated and maintained by aservice provider in operational communication with an assay instrument, desktop, laptop and / or server system via an Internet connection or network connection.

[0163] In some embodiments, a hardware platform for providing a computational environment comprises a processor (i.e., CPU) wherein processor time and memory layout such as random access memory (i.e., RAM) are systems considerations. For example, smaller computer systems offer inexpensive, fast processors and large memory and storage capabilities. In some embodiments, graphics processing units (GPUs) can be used. In some embodiments, hardware platforms for performing computational methods as described herein comprise one or more computer systems with one or more processors. In some embodiments, smaller computers are clustered together to yield a supercomputer network.

[0164] In some embodiments, computational methods as described herein are carried out on a collection of inter- or intra-connected computer systems (i.e., grid technology) which may run a variety of operating systems in a coordinated manner. For example, the CONDOR framework (University of Wisconsin-Madison) and systems available through United Devices are exemplary of the coordination of multiple stand-alone computer systems for the purpose dealing with large amounts of data. These systems may offer Perl interfaces to submit, monitor and manage large sequence analysis jobs on a cluster in serial or parallel configurations. EXAMPLES

[0165] Some aspects of the embodiments discussed above are disclosed in further detail in the following examples, which are not in any way intended to limit the scope of the present disclosure. Those in the art will appreciate that many other embodiments also fall within the scope of the disclosure, as it is described herein above and in the claims. Example 1

[0166] In the following example, the RCCX copy number was estimated for a number of nucleic acid samples (N = 204). The estimated RCCX copy number was compared to orthogonal sequencing results. Estimating a copy number of RCCX modules

[0167] The number of copies of the RCCX modules was estimated by counting reads belonging to both copies of the segmental duplication. In most cases, reads could not be unambiguously mapped to either copy of the repeat due to high sequence homology, but all reads placed in either copy were counted to provide a more accurate measure of the summed copy number of both regions. The region used for copy number calling covered most, but not all, of the RCCX region. The region used for copy number calling began after a polymorphic 6.4 kb HERV-K retrotransposon in introns of both C4A and C4B and extended 20 kb downstream to a 120 bp deletion in TNXA, including the entirety of the CYP21A2 gene. The region corresponded to positions chr6: 32024461-chr6:32043719 and chr6: 31991723-chr6: 32010985 of reference genome hg38. The copy number calling subregion of RCCX was therefore large enough to reach any nonallelic homologous recombination events (which, affecting a whole copy of RCCX, are 30 kb in length). Read coverage was corrected for GC content by normalization against a panel of approximately 3,000 preselected genomic sites approximately 2 kb in length, with highly consistent diploid copy number. The normalized sequence read count was then binned using a Gaussian mixture model. This estimated copy number was an accurate estimate of the total copy number of the RCCX segmental duplication.

[0168] The RCCX copy number calling results were compared to RCCX copy number calls from the orthogonal BIONANO GENOMICS, INC. (San Diego, CA) optical mapping technology in 204 genomes from the 1000 Genomes Project cohort. The results are depicted in FIG.4. Pearson’s correlation coefficient and P-value are annotated at bottom-right of FIG.4.

[0169] While optical mapping lacks the resolution to identify gene fusions or small variation, these call comparisons indicated the overall copy number calling accuracy of the methods described. In 201 of 204 genomes, the copy number calls agreed, while in three genomes there was a disagreement of one RCCX copy. This concordance demonstrates the high accuracy of the methods described in estimating the copy number of the RCCX modules. Example 2

[0170] In the following example, a copy number of a TNXB gene located within a RCCX region of a human genome was estimated for a nucleic acid sample HG00103.Methods

[0171] Sequence reads were obtained from the nucleic acid sample. A copy number was estimated for RCCX modules within the human genome of the nucleic acid sample using the methods described in Example 1 above.

[0172] Sequence reads which included a TNXA-specific base at one or more pre- determined differentiating sites of a TNXB gene and a TNXA pseudogene within the RCCX region were counted to obtain a TNXA sequence read count. Sequence reads which included a TNXB-specific base at the one or more pre-determined differentiating sites were counted to obtain a TNXB sequence read count.

[0173] The copy number of the TNXB gene was estimated based on the estimated copy number of RCCX modules, the TNXB sequence read count, and the TNXA sequence read count. First, certain quality control steps were performed for each sample to check for missing and / or zero coverage. If the count of sequence reads which included a TNXB-specific base was greater than expected if (CN=0 + random error), a copy number of 0 for the TNXB gene was estimated. If the estimated copy number of RCCX modules was missing for the sample, a copy number of the TNXB gene was not estimated. If the estimated copy number of RCCX modules was 0, a copy number of 0 was estimated for the TNXB gene.

[0174] For samples which passed the quality checks detailed above, a Poisson posterior probability of a TNXB copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules was calculated based on the TNXB sequence read count and the TNXA sequence read count, and based on a prior probability of a TNXB copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules. Results

[0175] The estimated copy number of RCCX modules was 3. The TNXA sequence read count was 237. The TNXB sequence read count was 110. For each possible copy number of the TNXB gene (0, 1, 2, or 3), a Poisson posterior probability of the TNXB copy number (TNXB CN) was calculated, given the estimated copy number of RCCX modules, the TNXB sequence read count, and the TNXA sequence read count. The results of the calculated probabilities are shown in the table below.Table 1 TNXB CN Probability 0 1.8x10-1721 3.3x10-22 2.5x10-193 3.6x10-49

[0176] Based on these calculated probabilities, the copy number of the TNXB gene for the nucleic acid sample was reported to be 1, which corresponded to the highest calculated probability. Example 3

[0177] In the following example, a copy number of a TNXB gene located within a RCCX region of a human genome was estimated for 3207 samples.

[0178] 14 samples were obtained from Radboud University Medical Center, 3 samples (TNXB Ehlers-Danlos Syndrome cases) were obtained from CMH, 3,190 samples were obtained from the 1000 Genomes Project (“1kG”). The copy number of the TNXB gene was estimated for each sample using the methods described in Example 2 above.

[0179] The copy number of the TNXB gene (TNXB CN) was estimated with high confidence (above 0.95) for 3060 / 3207 samples (95.4% of all samples). The number of samples with each estimated copy number of the TNXB gene is shown in the table below. Table 2 TNXB CN Number of Samples None 2 1 1* 2 3185 3 194 1 * NA19741, from 1kG. RCCX CN=3. Likely hypermobile Ehlers-Danlos case. Example 4

[0180] In the following example, unphased variant calling targeted to the TNXB gene / TNXA pseudogene was performed for the samples of Example 3.

[0181] For each sample, sequence reads were obtained which had been determined from the nucleic acid sample. From those sequence reads, sequence reads which align to a site of a single-nucleotide variant or indel (collectively, “small variant”) in a TNXB gene in a human genome of the nucleic acid sample were obtained. Sequence reads which align to a corresponding site of the single-nucleotide variant or indel in a TNXA pseudogene in human genome of the nucleic acid sample were also obtained. The small variants included TNXB_c.12220C>T and TNXB_c.10623_10624del.

[0182] Sequence reads which contained a base corresponding to an alternative allele at the site of the single-nucleotide variant or indel were counted. Counting sequence reads included counting sequence reads which aligned to the TNXB gene and sequence reads which aligned to the TNXA pseudogene.

[0183] Sequence reads which contained a base corresponding to a reference allele at the site of the single-nucleotide variant or indel were counted. Counting sequence reads included counting sequence reads which aligned to the TNXB gene and sequence reads which aligned to the TNXA pseudogene.

[0184] Then, the single-nucleotide variant or indel was detected based on the count of the sequence reads which contain a base corresponding to an alternative allele and the count of sequence reads which contain a base corresponding to a reference allele. A variant call was reported, and the variant call did not specify whether the detected small variant was in the TNXB gene or the TNXA pseudogene.

[0185] A carrier variant call was made for four samples, HG02772 (TNXB_c.12220C>T, mother), HG02773 (TNXB_c.12220C>T, child), HG03548 (TNXB_c.12220C>T), and HG03770 (TNXB_c.10623_10624del). Follow-up long read sequencing was performed for each of these samples to confirm phasing / placement of the detected small variant.Example 5

[0186] In the following example, a copy number of a C4A gene or a C4B gene located within a RCCX region of a human genome was estimated for the samples of Example 3. Methods

[0187] Sequence reads were obtained from the nucleic acid sample. A copy number was estimated for RCCX modules within the human genome of the nucleic acid sample using the methods described in Example 1 above.

[0188] Sequence reads which included a C4A-specific base at one or more pre- determined differentiating sites of a C4A gene and a C4B gene within the RCCX region were counted to obtain a C4A sequence read count. Sequence reads which included a C4B-specific base at the one or more pre-determined differentiating sites were counted to obtain a C4B sequence read count.

[0189] The copy number of the C4A gene and the copy number of the C4B gene were estimated based on the estimated copy number of RCCX modules, the C4A sequence read count, and the C4B sequence read count. First, certain quality control steps were performed for each sample to check for missing and / or zero coverage. If the count of sequence reads which included a C4A-specific base or C4B-specific base was greater than expected if (CN=0 + random error), a copy number of 0 for the C4A gene or the C4B gene, respectively, was estimated. If the estimated copy number of RCCX modules was missing for the sample, a copy number of the C4A gene or the C4B gene were not estimated. If the estimated copy number of RCCX modules was 0, a copy number of 0 was estimated for the C4A gene and the C4B gene.

[0190] For samples which passed the quality checks detailed above, a Poisson posterior probability of a C4A copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules was calculated based on the C4A sequence read count and the C4B sequence read count, and based on a prior probability of a C4A copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules.

[0191] Similarly, a Poisson posterior probability of a C4B copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules was calculated based on the C4A sequence read count and the C4B sequence read count, and based on a priorprobability of a C4B copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules. Results

[0192] The copy number of the C4A gene (C4A CN) and the copy number of the C4B gene (C4B CN) were estimated with high confidence (above 0.95) for 2878 / 3207 samples (89.7% of all samples). The number of samples with each estimated copy number of the C4A gene, the C4B gene, and combined C4A gene and C4 gene (total C4 CN) is shown in the table below. Table 3 CN C4A CN samples C4B CN samples Total C4 CN None 2 2 2 0 1 49 - 1 323 811 - 2 2124 1846 54 3 684 424 574 4 66 68 1886 5 8 8 535 6 - - 135 7 - - 21 8 - - 1 Example 6

[0193] In the following example, unphased variant calling targeted to the C4A gene / C4B gene was performed for the samples of Example 3.

[0194] For each sample, sequence reads were obtained which had been determined from the nucleic acid sample. From those sequence reads, sequence reads which aligned to asite of a single-nucleotide variant or indel (collectively, “small variant”) in a C4A gene or a C4B gene in a human genome of the nucleic acid sample were obtained. Sequence reads which align to a corresponding site of the single-nucleotide variant or indel in the paralog C4A gene or C4B gene in human genome of the nucleic acid sample were also obtained. The small variants included C4A_insCT and C4B_3676G>A.

[0195] Sequence reads which contained a base corresponding to an alternative allele at the site of the single-nucleotide variant or indel were counted. Counting sequence reads included counting sequence reads which aligned to the C4A gene and sequence reads which aligned to the C4B gene.

[0196] Sequence reads which contained a base corresponding to a reference allele at the site of the single-nucleotide variant or indel were counted. Counting sequence reads included counting sequence reads which aligned to the C4A gene and sequence reads which aligned to the C4B pseudogene.

[0197] Then, the single-nucleotide variant or indel was detected based on the count of the sequence reads which contain a base corresponding to an alternative allele and the count of sequence reads which contain a base corresponding to a reference allele. A variant call was reported, and the variant call did not specify whether the detected small variant was in the C4A gene or the C4B gene.

[0198] 17 samples were determined to be carriers of the C4A_insCT indel. These samples are listed below in Table 4. Table 4 C4A variant carriers HG00103 HG00145 HG00148 HG00185 HG00231 HG00282 HG00323 HG00372HG00376 HG00378 HG00551 HG01334 HG01673 HG01674 NA10835 NA12249 NA12775

[0199] No samples were determined to be carriers of the C4B_3676G>A single- nucleotide variant. Other Considerations

[0200] The embodiments described herein are exemplary. Modifications, rearrangements, substitute processes, etc. may be made to these embodiments and still be encompassed within the teachings set forth herein. One or more of the steps, processes, or methods described herein may be carried out by one or more processing and / or digital devices, suitably programmed.

[0201] The various illustrative imaging or data processing techniques described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. To illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. The described functionality can be implemented in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosure.

[0202] The various illustrative detection systems described in connection with the embodiments disclosed herein can be implemented or performed by a machine, such as a processor configured with specific instructions, a digital signal processor (DSP), an applicationspecific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A processor can be a microprocessor, but in the alternative, the processor can be a controller, microcontroller, or state machine, combinations of the same, or the like. A processor can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. For example, systems described herein may be implemented using a discrete memory chip, a portion of memory in a microprocessor, flash, EPROM, or other types of memory.

[0203] The elements of a method, process, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of computer-readable storage medium known in the art. An exemplary storage medium can be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. A software module can comprise computer-executable instructions which cause a hardware processor to execute the computer- executable instructions.

[0204] Conditional language used herein, such as, among others, “can,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and / or states. Thus, such conditional language is not generally intended to imply that features, elements and / or states are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and / or states are included or are to be performed in any particular embodiment. The terms “comprising,” “including,” “having,” “involving,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list.

[0205] Disjunctive language such as the phrase “at least one of X, Y or Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y or Z, or any combination thereof (such as X, Y and / or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y or at least one of Z to each be present.

[0206] The terms “about” or “approximate” and the like are synonymous and are used to indicate that the value modified by the term has an understood range associated with it, where the range can be ±20%, ±15%, ±10%, ±5%, or ±1%. The term “substantially” is used to indicate that a result (such as a measurement value) is close to a targeted value, where close can mean, for example, the result is within 80% of the value, within 90% of the value, within 95% of the value, or within 99% of the value.

[0207] Unless otherwise explicitly stated, articles such as “a” or “an” should generally be interpreted to include one or more described items. Accordingly, phrases such as “a device configured to” or “a device to” are intended to include one or more recited devices. Such one or more recited devices can also be collectively configured to carry out the stated recitations. For example, “a processor to carry out recitations A, B and C” can include a first processor configured to carry out recitation A working in conjunction with a second processor configured to carry out recitations B and C.

[0208] While the above detailed description has shown, described, and pointed out novel features as applied to illustrative embodiments, it will be understood that various omissions, substitutions, and changes in the form and details of the devices or algorithms illustrated can be made without departing from the spirit of the disclosure. As will be recognized, certain embodiments described herein can be embodied within a form that does not provide all of the features and benefits set forth herein, as some features can be used or practiced separately from others. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.

[0209] It should be appreciated that all combinations of the foregoing concepts (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein.

[0210] The scope of the present disclosure is not intended to be limited by the specific disclosures of examples in this section or elsewhere in this specification, and may be defined by claims as presented in this section or elsewhere in this specification or as presented in the future. The language of the claims is to be interpreted broadly based on the language employed in the claims and not limited to the examples described in the present specification or during the prosecution of the application, which examples are to be construed as non- exclusive.

Claims

WHAT IS CLAIMED IS:

1. A computer-implemented method of estimating a copy number of a TNXB gene located within a RCCX region of a human genome, comprising: estimating a copy number of RCCX modules in a nucleic acid sample from a human genome; counting sequence reads which include a TNXA-specific base at one or more pre-determined differentiating sites of a TNXB gene and a TNXA pseudogene within the RCCX region to obtain a TNXA sequence read count; counting sequence reads which include a TNXB-specific base at the one or more pre-determined differentiating sites to obtain a TNXB sequence read count; and estimating the copy number of the TNXB gene based on the estimated copy number of RCCX modules, the TNXB sequence read count, and the TNXA sequence read count.

2. The method of claim 1, wherein estimating the copy number of the TNXB gene comprises calculating a probability of a TNXB copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules, based on the TNXB sequence read count and the TNXA sequence read count.

3. The method of claim 2, wherein estimating the copy number of the TNXB gene comprises calculating a probability of a TNXB copy number based on a prior probability of a TNXB copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules.

4. The method of claim 2 or claim 3, wherein estimating the copy number of a TNXB gene comprises calculating a Poisson posterior probability.

5. The method of any one of claims 1-4, wherein estimating a copy number of RCCX modules comprises counting sequence reads which align to an RCCX region of the human genome.

6. The method of claim 5, wherein estimating a copy number of RCCX modules comprises counting sequence reads which align to a C4A gene, a CYP21A1P gene, a TNXA pseudogene, a C4B gene, a CYP21A2 gene, or a TNXB gene in the human genome.

7. The method of claim 6, wherein estimating a copy number of RCCX modules comprises counting sequence reads which align to a region corresponding to positions chr6:32024461-chr6:32043719 of reference genome hg38, chr6: 31991723-chr6: 32010985 of reference genome hg38, chr6:31992238-chr6: 32011496 of reference genome hg19, or chr6:31959500-chr6:31978762 of reference genome hg19.

8. The method of any of claims 5-7, wherein the estimating the copy number of RCCX modules comprises counting sequence reads which align to diploid regions in the human genome within the nucleic acid sample.

9. The method of claim 8, wherein estimating the copy number of RCCX modules comprises normalizing the count of the sequence reads which align to a RCCX region of the human genome by the sequence reads which align to a plurality of diploid regions of the human genome.

10. The method of claim 9, wherein said normalizing comprises GC correction.

11. The method of claim 9 or claim 10, wherein estimating a copy number of RCCX modules comprises binning the normalized count of the sequence reads which align to a RCCX region of the human genome using a Gaussian mixture model.

12. The method of any one of claims 1-11, further comprising creating a digital file including the estimated copy number of the TNXB gene.

13. The method of any one of claims 1-12, wherein the one or more of pre-determined differentiating sites comprise a site corresponding to chr6:32046056 of the TNXB gene or corresponding position chr6:32013202 in the TNXA pseudogene, in reference genome hg38.

14. A computer-implemented method of detecting one or more single-nucleotide variants or indels in a TNXB gene or a TNXA pseudogene in a nucleic acid sample, the method comprising: determining sequence reads from the nucleic acid sample; obtaining sequence reads which align to a site of a single-nucleotide variant or indel in a TNXB gene in a human genome of the nucleic acid sample; obtaining sequence reads which align to a corresponding site of the single- nucleotide variant or indel in a TNXA pseudogene in the human genome of the nucleic acid sample; counting sequence reads which contain a base corresponding to an alternative allele at the site of the single-nucleotide variant or indel, wherein counting sequencereads comprises counting sequence reads which align to the TNXB gene and sequence reads which align to the TNXA pseudogene; counting sequence reads which contain a base corresponding to a reference allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the TNXB gene and sequence reads which align to the TNXA pseudogene; and based on the count of the sequence reads which contain a base corresponding to an alternative allele and the count of sequence reads which contain a base corresponding to a reference allele, detecting the single-nucleotide variant or indel, wherein the detection of the single-nucleotide variant or indel is not specific to the TNXB gene or the TNXA pseudogene.

15. The method of claim 14, wherein the sequence reads are less than 400 bp in length.

16. The method of claim 14 or claim 15, wherein the one or more single-nucleotide variants or indels comprise TNXB_c.12220C>T or TNXB_c.10623_10624del.

17. The method of any one of claims 14-16, further comprising creating a digital file including a variant call corresponding to the single-nucleotide variant or indel.

18. A computer-implemented method of estimating a copy number of a C4A gene or a C4B gene located within a RCCX region of a human genome, the method comprising: estimating a copy number of RCCX modules in a nucleic acid sample from a human genome; counting sequence reads which include a C4A-specific base at one or more pre- determined differentiating sites of a C4A gene and a C4B gene within the RCCX region to obtain a C4A sequence read count; counting sequence reads which include a C4B-specific base at the one or more pre-determined differentiating sites to obtain a C4B sequence read count; and estimating the copy number of the C4A gene or the copy number of the C4B gene based on the estimated copy number of RCCX modules, the C4A sequence read count, and the C4B sequence read count.

19. The method of claim 18, wherein estimating the copy number of the C4A gene or the copy number of the C4B gene comprises calculating a probability of a C4A copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules, based onthe C4A sequence read count and the C4B sequence read count, or calculating a probability of a C4B copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules, based on the C4A sequence read count and the C4B sequence read count.

20. The method of claim 19, wherein estimating the copy number of the C4A gene or the copy number of the C4B gene comprises calculating a probability of a C4A copy number or a C4B copy number based on a prior probability of a C4A copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules, or a prior probability of a C4B copy number of 0 up to a number corresponding to the estimated copy number of RCCX modules.

21. The method of claim 19 or claim 20 wherein estimating the copy number of the C4A gene or the copy number of the C4B gene comprises a calculating Poisson posterior probability.

22. The method of any of claims 18-21, wherein estimating a copy number of RCCX modules comprises counting sequence reads which align to the RCCX region of the human genome.

23. The method of claim 22, wherein estimating a copy number of RCCX modules comprises counting sequence reads which align to a C4A gene, a CYP21A1P gene, a TNXA pseudogene, a C4B gene, a CYP21A2 gene, or a TNXB gene in the human genome.

24. The method of claim 23, wherein estimating a copy number of RCCX modules comprises counting sequence reads which align to a region corresponding to positions chr6: 32024461-chr6:32043719 of reference genome hg38, chr6: 31991723-chr6: 32010985 of reference genome hg38, chr6:31992238-chr6: 32011496 of reference genome hg19, or chr6:31959500-chr6:31978762 of reference genome hg19.

25. The method of any of claims 22-24, wherein estimating the copy number of the C4A gene or the copy number of the C4B gene further comprises counting sequence reads which align to diploid regions in the human genome within the nucleic acid sample.

26. The method of claim 25, wherein estimating a copy number of RCCX modules comprises normalizing the count of the sequence reads which align to a RCCX region of the human genome by the count of sequence reads which align to a plurality of diploid regions of the human genome.

27. The method of claim 26, wherein said wherein said normalizing comprises GC correction.

28. The method of claim 26 or claim 27, wherein estimating a copy number of RCCX modules comprises binning the normalized count of the sequence reads which align to a RCCX region of the human genome using a Gaussian mixture model.

29. The method of any one of claims 18-28, further comprising creating a digital file including the estimated copy number of the C4A gene or the estimated copy number of the C4B gene.

30. The method of any one of claims 18-29, wherein the one or more pre-determined differentiating sites comprise a site corresponding to chr6:32028821, chr6:32028824, chr6:32028832, chr6:32028835, or chr6: 32028837of the C4B gene or a corresponding position in the C4A gene, in reference genome hg38.

31. A computer-implemented method of detecting one or more single-nucleotide variants or indels in a C4A gene or a C4B gene in a nucleic acid sample, the method comprising: determining sequence reads from the nucleic acid sample; obtaining sequence reads which align to a site of a single-nucleotide variant or indel in a C4A gene in a human genome of the nucleic acid sample; obtaining sequence reads which align to a corresponding site of the single- nucleotide variant or indel in a C4B gene in the human genome of the nucleic acid sample; counting sequence reads which contain a base corresponding to an alternative allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the C4A gene and sequence reads which align to the C4B gene; counting sequence reads which contain a base corresponding to a reference allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the C4A gene and sequence reads which align to the C4B gene; and based on the count of the sequence reads which contain a base corresponding to an alternative allele and the count of sequence reads which contain a base corresponding to a reference allele, detecting the single-nucleotide variant or indel,wherein the detection of the single-nucleotide variant or indel is not specific to the C4A gene or the C4B gene.

32. The method of claim 31, wherein the sequence reads are less than 400 bp in length.

33. The method of claim 31 or claim 32, wherein the one or more single-nucleotide variants or indels comprise C4A_insCT or C4B_3676G>A.

34. The method of any one of claims 31-33, further comprising creating a digital file including a variant call corresponding to the single-nucleotide variant or indel.

35. An electronic system for estimating a copy number of a TNXB gene located within a RCCX region of a human genome, comprising a processor configured to perform a method comprising: estimating a copy number of RCCX modules in a nucleic acid sample from a human genome; counting sequence reads which include a TNXA-specific base at one or more pre-determined differentiating sites of a TNXB gene and a TNXA pseudogene within the RCCX region to obtain a TNXA sequence read count; counting sequence reads which include a TNXB-specific base at the one or more pre-determined differentiating sites to obtain a TNXB sequence read count; and estimating the copy number of the TNXB gene based on the estimated copy number of RCCX modules, the TNXB sequence read count, and the TNXA sequence read count.

36. An electronic system for detecting one or more single-nucleotide variants or indels in a TNXB gene or a TNXA pseudogene in a nucleic acid sample, comprising a processor configured to perform a method comprising: determining sequence reads from the nucleic acid sample; obtaining sequence reads which align to a site of a single-nucleotide variant or indel in a TNXB gene in a human genome of the nucleic acid sample; obtaining sequence reads which align to a corresponding site of the single- nucleotide variant or indel in a TNXA pseudogene in human genome of the nucleic acid sample; counting sequence reads which contain a base corresponding to an alternative allele at the site of the single-nucleotide variant or indel, wherein counting sequencereads comprises counting sequence reads which align to the TNXB gene and sequence reads which align to the TNXA pseudogene; counting sequence reads which contain a base corresponding to a reference allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the TNXB gene and sequence reads which align to the TNXA pseudogene; and based on the count of the sequence reads which contain a base corresponding to an alternative allele and the count of sequence reads which contain a base corresponding to a reference allele, detecting the single-nucleotide variant or indel, wherein the detection of the single-nucleotide variant or indel is not specific to the TNXB gene or the TNXA pseudogene.

37. An electronic system for estimating a copy number of a C4A gene or a C4B gene located within a RCCX region of a human genome, comprising a processor configured to perform a method comprising: estimating a copy number of RCCX modules in a nucleic acid sample from a human genome; counting sequence reads which include a C4A-specific base at one or more pre- determined differentiating sites of a C4A gene and a C4B gene within the RCCX region to obtain a C4A sequence read count; counting sequence reads which include a C4B-specific base at the one or more pre-determined differentiating sites to obtain a C4B sequence read count; and estimating the copy number of the C4A gene or the copy number of the C4B gene based on the estimated copy number of RCCX modules, the C4A sequence read count, and the C4B sequence read count.

38. An electronic system for detecting one or more single-nucleotide variants or indels in a C4A gene or a C4B gene in a nucleic acid sample, comprising a processor configured to perform a method comprising: determining sequence reads from the nucleic acid sample; obtaining sequence reads which align to a site of a single-nucleotide variant or indel in a C4A gene in a human genome of the nucleic acid sample;obtaining sequence reads which align to a corresponding site of the single- nucleotide variant or indel in a C4B gene in the human genome of the nucleic acid sample; counting sequence reads which contain a base corresponding to an alternative allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the C4A gene and sequence reads which align to the C4B gene; counting sequence reads which contain a base corresponding to a reference allele at the site of the single-nucleotide variant or indel, wherein counting sequence reads comprises counting sequence reads which align to the C4A gene and sequence reads which align to the C4B gene; and based on the count of the sequence reads which contain a base corresponding to an alternative allele and the count of sequence reads which contain a base corresponding to a reference allele, detecting the single-nucleotide variant or indel, wherein the detection of the single-nucleotide variant or indel is not specific to the C4A gene or the C4B gene.

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